Identification-of-Pulsar-stars.r
devikarajasekar
2022-12-15
# INSTALLING NECESSARY PACKAGES
# install.packages("caret", dependencies = TRUE)
# install.packages("skimr")
# install.packages("corrplot")
# install.packages("mice")
# install.packages('tidyverse')
# install.packages('ggplot2')
# install.packages('neuralnet')
# INCLUDING LIBRARIES
library(caret)## Loading required package: ggplot2## Loading required package: latticelibrary(mice)##
## Attaching package: 'mice'## The following object is masked from 'package:stats':
##
## filter## The following objects are masked from 'package:base':
##
## cbind, rbindlibrary(skimr)
library(corrplot)## corrplot 0.92 loadedlibrary(tidyverse)## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ✔ purrr 0.3.4## Warning: package 'tidyr' was built under R version 4.0.5## Warning: package 'readr' was built under R version 4.0.5## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks mice::filter(), stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ purrr::lift() masks caret::lift()library(ggplot2)
library(neuralnet)##
## Attaching package: 'neuralnet'
##
## The following object is masked from 'package:dplyr':
##
## computelibrary(nnet)
# LOADING THE DATASET
df_train = read.csv('/Users/devikarajasekar/Documents/SCHOOL/Identification of Pulsar stars/Project/pulsar_data_train.csv')
df_test = NULL
# CLEANING and TRANSFORMING the dataset
#Before imputing NULL values
str(df_train)## 'data.frame': 12528 obs. of 9 variables:
## $ Mean.of.the.integrated.profile : num 121.2 77 130.6 156.4 84.8 ...
## $ Standard.deviation.of.the.integrated.profile: num 48.4 36.2 53.2 48.9 36.1 ...
## $ Excess.kurtosis.of.the.integrated.profile : num 0.375 0.713 0.133 -0.216 0.825 ...
## $ Skewness.of.the.integrated.profile : num -0.0132 3.3887 -0.2972 -0.1713 3.2741 ...
## $ Mean.of.the.DM.SNR.curve : num 3.17 2.4 2.74 17.47 2.79 ...
## $ Standard.deviation.of.the.DM.SNR.curve : num 18.4 17.6 22.4 NA 20.6 ...
## $ Excess.kurtosis.of.the.DM.SNR.curve : num 7.45 9.41 8.51 2.96 8.41 ...
## $ Skewness.of.the.DM.SNR.curve : num 65.2 102.7 74 7.2 76.3 ...
## $ target_class : num 0 0 0 0 0 0 0 0 0 0 ... head(df_train)## Mean.of.the.integrated.profile Standard.deviation.of.the.integrated.profile
## 1 121.15625 48.37297
## 2 76.96875 36.17556
## 3 130.58594 53.22953
## 4 156.39844 48.86594
## 5 84.80469 36.11766
## 6 121.00781 47.17694
## Excess.kurtosis.of.the.integrated.profile Skewness.of.the.integrated.profile
## 1 0.3754847 -0.01316549
## 2 0.7128979 3.38871856
## 3 0.1334083 -0.29724164
## 4 -0.2159886 -0.17129365
## 5 0.8250128 3.27412537
## 6 0.2297081 0.09133623
## Mean.of.the.DM.SNR.curve Standard.deviation.of.the.DM.SNR.curve
## 1 3.168896 18.39937
## 2 2.399666 17.57100
## 3 2.743311 22.36255
## 4 17.471572 NA
## 5 2.790134 20.61801
## 6 2.036789 NA
## Excess.kurtosis.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve target_class
## 1 7.449874 65.159298 0
## 2 9.414652 102.722975 0
## 3 8.508364 74.031324 0
## 4 2.958066 7.197842 0
## 5 8.405008 76.291128 0
## 6 9.546051 112.131721 0 summary(df_train)## Mean.of.the.integrated.profile Standard.deviation.of.the.integrated.profile
## Min. : 5.812 Min. :24.77
## 1st Qu.:100.871 1st Qu.:42.36
## Median :115.184 Median :46.93
## Mean :111.042 Mean :46.52
## 3rd Qu.:127.109 3rd Qu.:50.98
## Max. :189.734 Max. :91.81
##
## Excess.kurtosis.of.the.integrated.profile Skewness.of.the.integrated.profile
## Min. :-1.7380 Min. :-1.7919
## 1st Qu.: 0.0247 1st Qu.:-0.1881
## Median : 0.2237 Median : 0.2033
## Mean : 0.4785 Mean : 1.7784
## 3rd Qu.: 0.4731 3rd Qu.: 0.9324
## Max. : 8.0695 Max. :68.1016
## NA's :1735
## Mean.of.the.DM.SNR.curve Standard.deviation.of.the.DM.SNR.curve
## Min. : 0.2132 Min. : 7.37
## 1st Qu.: 1.9105 1st Qu.: 14.40
## Median : 2.7926 Median : 18.41
## Mean : 12.6748 Mean : 26.35
## 3rd Qu.: 5.4132 3rd Qu.: 28.34
## Max. :222.4214 Max. :110.64
## NA's :1178
## Excess.kurtosis.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## Min. :-3.139 Min. : -1.977
## 1st Qu.: 5.803 1st Qu.: 35.200
## Median : 8.451 Median : 83.126
## Mean : 8.333 Mean : 105.526
## 3rd Qu.:10.728 3rd Qu.: 139.998
## Max. :34.540 Max. :1191.001
## NA's :625
## target_class
## Min. :0.00000
## 1st Qu.:0.00000
## Median :0.00000
## Mean :0.09203
## 3rd Qu.:0.00000
## Max. :1.00000
## # Generating a summary of the dataset with histograms
skimmed <- skim(df_train)
skimmed| Name | df_train |
| Number of rows | 12528 |
| Number of columns | 9 |
| _______________________ | |
| Column type frequency: | |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Mean.of.the.integrated.profile | 0 | 1.00 | 111.04 | 25.67 | 5.81 | 100.87 | 115.18 | 127.11 | 189.73 | ▁▁▇▇▁ |
| Standard.deviation.of.the.integrated.profile | 0 | 1.00 | 46.52 | 6.80 | 24.77 | 42.36 | 46.93 | 50.98 | 91.81 | ▂▇▂▁▁ |
| Excess.kurtosis.of.the.integrated.profile | 1735 | 0.86 | 0.48 | 1.06 | -1.74 | 0.02 | 0.22 | 0.47 | 8.07 | ▇▇▁▁▁ |
| Skewness.of.the.integrated.profile | 0 | 1.00 | 1.78 | 6.21 | -1.79 | -0.19 | 0.20 | 0.93 | 68.10 | ▇▁▁▁▁ |
| Mean.of.the.DM.SNR.curve | 0 | 1.00 | 12.67 | 29.61 | 0.21 | 1.91 | 2.79 | 5.41 | 222.42 | ▇▁▁▁▁ |
| Standard.deviation.of.the.DM.SNR.curve | 1178 | 0.91 | 26.35 | 19.61 | 7.37 | 14.40 | 18.41 | 28.34 | 110.64 | ▇▁▁▁▁ |
| Excess.kurtosis.of.the.DM.SNR.curve | 0 | 1.00 | 8.33 | 4.54 | -3.14 | 5.80 | 8.45 | 10.73 | 34.54 | ▂▇▂▁▁ |
| Skewness.of.the.DM.SNR.curve | 625 | 0.95 | 105.53 | 107.40 | -1.98 | 35.20 | 83.13 | 140.00 | 1191.00 | ▇▁▁▁▁ |
| target_class | 0 | 1.00 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
sum(is.na.data.frame(df_train))## [1] 3538 X <- df_train[,1:8]
y <- df_train[,9]
md.pattern(X) #returns a tabular form of missing value present in each variable in a data set
## Mean.of.the.integrated.profile
## 9273 1
## 1509 1
## 986 1
## 135 1
## 482 1
## 86 1
## 52 1
## 5 1
## 0
## Standard.deviation.of.the.integrated.profile
## 9273 1
## 1509 1
## 986 1
## 135 1
## 482 1
## 86 1
## 52 1
## 5 1
## 0
## Skewness.of.the.integrated.profile Mean.of.the.DM.SNR.curve
## 9273 1 1
## 1509 1 1
## 986 1 1
## 135 1 1
## 482 1 1
## 86 1 1
## 52 1 1
## 5 1 1
## 0 0
## Excess.kurtosis.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 9273 1 1
## 1509 1 1
## 986 1 1
## 135 1 1
## 482 1 0
## 86 1 0
## 52 1 0
## 5 1 0
## 0 625
## Standard.deviation.of.the.DM.SNR.curve
## 9273 1
## 1509 1
## 986 0
## 135 0
## 482 1
## 86 1
## 52 0
## 5 0
## 1178
## Excess.kurtosis.of.the.integrated.profile
## 9273 1 0
## 1509 0 1
## 986 1 1
## 135 0 2
## 482 1 1
## 86 0 2
## 52 1 2
## 5 0 3
## 1735 3538 # IMPUTING THE DATASET
imputed_Data <- mice(X, m=5, maxit = 50, method = 'pmm', seed = 500)##
## iter imp variable
## 1 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 1 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 1 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 1 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 1 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 2 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 2 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 2 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 2 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 2 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 3 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 3 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 3 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 3 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 3 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 4 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 4 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 4 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 4 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 4 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 5 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 5 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 5 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 5 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 5 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 6 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 6 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 6 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 6 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 6 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 7 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 7 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 7 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 7 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 7 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 8 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 8 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 8 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 8 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 8 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 9 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 9 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 9 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 9 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 9 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 10 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 10 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 10 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 10 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 10 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 11 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 11 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 11 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 11 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 11 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 12 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 12 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 12 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 12 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 12 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 13 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 13 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 13 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 13 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 13 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 14 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 14 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 14 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 14 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 14 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 15 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 15 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 15 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 15 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 15 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 16 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 16 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 16 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 16 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 16 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 17 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 17 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 17 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 17 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 17 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 18 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 18 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 18 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 18 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 18 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 19 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 19 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 19 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 19 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 19 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 20 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 20 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 20 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 20 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 20 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 21 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 21 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 21 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 21 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 21 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 22 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 22 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 22 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 22 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 22 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 23 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 23 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 23 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 23 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 23 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 24 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 24 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 24 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 24 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 24 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 25 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 25 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 25 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 25 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 25 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 26 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 26 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 26 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 26 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 26 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 27 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 27 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 27 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 27 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 27 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 28 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 28 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 28 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 28 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 28 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 29 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 29 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 29 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 29 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 29 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 30 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 30 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 30 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 30 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 30 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 31 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 31 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 31 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 31 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 31 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 32 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 32 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 32 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 32 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 32 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 33 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 33 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 33 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 33 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 33 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 34 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 34 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 34 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 34 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 34 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 35 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 35 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 35 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 35 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 35 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 36 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 36 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 36 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 36 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 36 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 37 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 37 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 37 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 37 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 37 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 38 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 38 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 38 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 38 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 38 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 39 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 39 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 39 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 39 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 39 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 40 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 40 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 40 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 40 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 40 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 41 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 41 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 41 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 41 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 41 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 42 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 42 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 42 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 42 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 42 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 43 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 43 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 43 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 43 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 43 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 44 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 44 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 44 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 44 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 44 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 45 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 45 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 45 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 45 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 45 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 46 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 46 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 46 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 46 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 46 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 47 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 47 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 47 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 47 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 47 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 48 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 48 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 48 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 48 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 48 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 49 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 49 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 49 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 49 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 49 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 50 1 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 50 2 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 50 3 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 50 4 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 50 5 Excess.kurtosis.of.the.integrated.profile Standard.deviation.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve summary(imputed_Data)## Class: mids
## Number of multiple imputations: 5
## Imputation methods:
## Mean.of.the.integrated.profile
## ""
## Standard.deviation.of.the.integrated.profile
## ""
## Excess.kurtosis.of.the.integrated.profile
## "pmm"
## Skewness.of.the.integrated.profile
## ""
## Mean.of.the.DM.SNR.curve
## ""
## Standard.deviation.of.the.DM.SNR.curve
## "pmm"
## Excess.kurtosis.of.the.DM.SNR.curve
## ""
## Skewness.of.the.DM.SNR.curve
## "pmm"
## PredictorMatrix:
## Mean.of.the.integrated.profile
## Mean.of.the.integrated.profile 0
## Standard.deviation.of.the.integrated.profile 1
## Excess.kurtosis.of.the.integrated.profile 1
## Skewness.of.the.integrated.profile 1
## Mean.of.the.DM.SNR.curve 1
## Standard.deviation.of.the.DM.SNR.curve 1
## Standard.deviation.of.the.integrated.profile
## Mean.of.the.integrated.profile 1
## Standard.deviation.of.the.integrated.profile 0
## Excess.kurtosis.of.the.integrated.profile 1
## Skewness.of.the.integrated.profile 1
## Mean.of.the.DM.SNR.curve 1
## Standard.deviation.of.the.DM.SNR.curve 1
## Excess.kurtosis.of.the.integrated.profile
## Mean.of.the.integrated.profile 1
## Standard.deviation.of.the.integrated.profile 1
## Excess.kurtosis.of.the.integrated.profile 0
## Skewness.of.the.integrated.profile 1
## Mean.of.the.DM.SNR.curve 1
## Standard.deviation.of.the.DM.SNR.curve 1
## Skewness.of.the.integrated.profile
## Mean.of.the.integrated.profile 1
## Standard.deviation.of.the.integrated.profile 1
## Excess.kurtosis.of.the.integrated.profile 1
## Skewness.of.the.integrated.profile 0
## Mean.of.the.DM.SNR.curve 1
## Standard.deviation.of.the.DM.SNR.curve 1
## Mean.of.the.DM.SNR.curve
## Mean.of.the.integrated.profile 1
## Standard.deviation.of.the.integrated.profile 1
## Excess.kurtosis.of.the.integrated.profile 1
## Skewness.of.the.integrated.profile 1
## Mean.of.the.DM.SNR.curve 0
## Standard.deviation.of.the.DM.SNR.curve 1
## Standard.deviation.of.the.DM.SNR.curve
## Mean.of.the.integrated.profile 1
## Standard.deviation.of.the.integrated.profile 1
## Excess.kurtosis.of.the.integrated.profile 1
## Skewness.of.the.integrated.profile 1
## Mean.of.the.DM.SNR.curve 1
## Standard.deviation.of.the.DM.SNR.curve 0
## Excess.kurtosis.of.the.DM.SNR.curve
## Mean.of.the.integrated.profile 1
## Standard.deviation.of.the.integrated.profile 1
## Excess.kurtosis.of.the.integrated.profile 1
## Skewness.of.the.integrated.profile 1
## Mean.of.the.DM.SNR.curve 1
## Standard.deviation.of.the.DM.SNR.curve 1
## Skewness.of.the.DM.SNR.curve
## Mean.of.the.integrated.profile 1
## Standard.deviation.of.the.integrated.profile 1
## Excess.kurtosis.of.the.integrated.profile 1
## Skewness.of.the.integrated.profile 1
## Mean.of.the.DM.SNR.curve 1
## Standard.deviation.of.the.DM.SNR.curve 1 df_train <- complete(imputed_Data,2)
# CHECKING FOR NA VALUES AFTER IMPUTATION
sum(is.na.data.frame(df_train))## [1] 0 # Append the Y variable
df_train$target_class <- y
df_train## Mean.of.the.integrated.profile
## 1 121.156250
## 2 76.968750
## 3 130.585938
## 4 156.398438
## 5 84.804688
## 6 121.007812
## 7 79.343750
## 8 109.406250
## 9 95.007812
## 10 109.156250
## 11 130.085938
## 12 96.937500
## 13 146.695312
## 14 128.734375
## 15 102.367188
## 16 125.609375
## 17 150.093750
## 18 104.562500
## 19 115.781250
## 20 133.125000
## 21 125.148438
## 22 99.281250
## 23 83.937500
## 24 135.132812
## 25 111.710938
## 26 127.328125
## 27 101.914062
## 28 108.742188
## 29 128.484375
## 30 102.234375
## 31 98.570312
## 32 36.710938
## 33 108.757812
## 34 117.679688
## 35 43.500000
## 36 122.093750
## 37 121.554688
## 38 107.703125
## 39 84.546875
## 40 126.562500
## 41 66.851562
## 42 100.078125
## 43 135.265625
## 44 105.273438
## 45 142.968750
## 46 107.171875
## 47 126.273438
## 48 128.343750
## 49 89.445312
## 50 110.062500
## 51 98.523438
## 52 107.070312
## 53 131.476562
## 54 95.679688
## 55 109.335938
## 56 120.593750
## 57 112.539062
## 58 114.828125
## 59 126.101562
## 60 126.242188
## 61 117.242188
## 62 134.804688
## 63 118.937500
## 64 130.218750
## 65 103.031250
## 66 96.312500
## 67 110.289062
## 68 118.140625
## 69 69.210938
## 70 120.031250
## 71 94.500000
## 72 144.750000
## 73 119.984375
## 74 126.554688
## 75 126.554688
## 76 130.226562
## 77 128.289062
## 78 139.140625
## 79 128.984375
## 80 95.007812
## 81 95.289062
## 82 105.312500
## 83 79.070312
## 84 131.773438
## 85 98.562500
## 86 131.960938
## 87 110.789062
## 88 136.062500
## 89 118.804688
## 90 128.812500
## 91 112.585938
## 92 132.664062
## 93 124.828125
## 94 103.007812
## 95 117.289062
## 96 147.710938
## 97 115.460938
## 98 116.234375
## 99 148.117188
## 100 101.632812
## 101 93.273438
## 102 133.273438
## 103 139.890625
## 104 122.429688
## 105 139.781250
## 106 124.867188
## 107 134.203125
## 108 121.726562
## 109 96.898438
## 110 109.679688
## 111 97.414062
## 112 128.890625
## 113 107.625000
## 114 96.218750
## 115 76.570312
## 116 145.835938
## 117 106.890625
## 118 140.429688
## 119 101.289062
## 120 138.804688
## 121 121.867188
## 122 86.562500
## 123 60.296875
## 124 120.726562
## 125 109.445312
## 126 105.851562
## 127 119.804688
## 128 123.710938
## 129 121.101562
## 130 123.828125
## 131 108.914062
## 132 128.632812
## 133 61.671875
## 134 128.257812
## 135 142.289062
## 136 151.609375
## 137 141.195312
## 138 110.023438
## 139 133.070312
## 140 100.976562
## 141 130.304688
## 142 115.085938
## 143 79.937500
## 144 127.804688
## 145 114.585938
## 146 130.937500
## 147 108.906250
## 148 114.554688
## 149 127.921875
## 150 144.085938
## 151 114.164062
## 152 115.890625
## 153 87.531250
## 154 130.781250
## 155 118.039062
## 156 125.367188
## 157 131.406250
## 158 109.203125
## 159 106.500000
## 160 9.742188
## 161 102.875000
## 162 95.882812
## 163 130.101562
## 164 135.078125
## 165 114.960938
## 166 133.476562
## 167 122.726562
## 168 84.335938
## 169 97.812500
## 170 107.546875
## 171 118.171875
## 172 115.078125
## 173 105.960938
## 174 109.617188
## 175 114.203125
## 176 119.546875
## 177 124.210938
## 178 95.929688
## 179 66.492188
## 180 108.125000
## 181 85.796875
## 182 121.765625
## 183 136.445312
## 184 150.250000
## 185 95.367188
## 186 143.710938
## 187 122.812500
## 188 122.335938
## 189 121.171875
## 190 36.468750
## 191 111.210938
## 192 133.085938
## 193 137.750000
## 194 107.328125
## 195 83.812500
## 196 121.843750
## 197 102.593750
## 198 77.984375
## 199 60.523438
## 200 104.015625
## 201 105.226562
## 202 126.984375
## 203 138.343750
## 204 98.468750
## 205 138.335938
## 206 130.632812
## 207 115.781250
## 208 114.484375
## 209 98.453125
## 210 111.070312
## 211 132.093750
## 212 133.132812
## 213 117.218750
## 214 144.125000
## 215 93.468750
## 216 106.234375
## 217 127.820312
## 218 114.171875
## 219 103.507812
## 220 120.171875
## 221 117.578125
## 222 105.968750
## 223 87.671875
## 224 130.148438
## 225 120.851562
## 226 83.039062
## 227 144.554688
## 228 41.773438
## 229 123.945312
## 230 139.640625
## 231 93.734375
## 232 128.226562
## 233 135.468750
## 234 125.945312
## 235 139.835938
## 236 101.656250
## 237 111.351562
## 238 142.085938
## 239 55.507812
## 240 71.554688
## 241 138.367188
## 242 115.500000
## 243 118.765625
## 244 133.523438
## 245 110.796875
## 246 116.539062
## 247 33.328125
## 248 103.007812
## 249 122.632812
## 250 114.921875
## 251 147.250000
## 252 111.140625
## 253 151.929688
## 254 108.890625
## 255 139.664062
## 256 29.953125
## 257 115.820312
## 258 128.226562
## 259 125.070312
## 260 148.250000
## 261 126.468750
## 262 132.695312
## 263 139.195312
## 264 112.585938
## 265 91.250000
## 266 113.492188
## 267 174.250000
## 268 128.460938
## 269 118.093750
## 270 133.859375
## 271 18.851562
## 272 82.789062
## 273 115.359375
## 274 90.125000
## 275 144.789062
## 276 101.617188
## 277 127.781250
## 278 125.609375
## 279 133.359375
## 280 143.828125
## 281 133.273438
## 282 126.062500
## 283 35.156250
## 284 128.921875
## 285 86.234375
## 286 93.554688
## 287 26.578125
## 288 127.359375
## 289 115.406250
## 290 127.109375
## 291 124.281250
## 292 121.359375
## 293 111.296875
## 294 118.867188
## 295 45.539062
## 296 129.312500
## 297 134.132812
## 298 88.250000
## 299 125.085938
## 300 148.164062
## 301 119.187500
## 302 87.062500
## 303 123.531250
## 304 113.484375
## 305 58.218750
## 306 84.304688
## 307 110.179688
## 308 120.726562
## 309 129.773438
## 310 107.304688
## 311 113.968750
## 312 126.625000
## 313 121.203125
## 314 101.062500
## 315 106.914062
## 316 107.554688
## 317 84.250000
## 318 89.921875
## 319 130.765625
## 320 144.687500
## 321 112.140625
## 322 53.531250
## 323 122.578125
## 324 115.093750
## 325 100.757812
## 326 128.882812
## 327 90.984375
## 328 136.164062
## 329 133.281250
## 330 57.992188
## 331 110.101562
## 332 121.476562
## 333 82.757812
## 334 114.406250
## 335 112.429688
## 336 16.554688
## 337 120.476562
## 338 17.031250
## 339 134.398438
## 340 109.218750
## 341 130.765625
## 342 137.218750
## 343 126.851562
## 344 129.617188
## 345 60.148438
## 346 124.132812
## 347 104.750000
## 348 128.523438
## 349 122.945312
## 350 81.492188
## 351 102.226562
## 352 117.148438
## 353 110.570312
## 354 146.992188
## 355 117.351562
## 356 144.367188
## 357 143.210938
## 358 80.953125
## 359 34.898438
## 360 150.296875
## 361 133.890625
## 362 106.742188
## 363 100.890625
## 364 98.437500
## 365 137.945312
## 366 65.281250
## 367 135.234375
## 368 115.226562
## 369 136.875000
## 370 98.320312
## 371 108.453125
## 372 107.328125
## 373 126.578125
## 374 104.414062
## 375 108.875000
## 376 119.695312
## 377 133.898438
## 378 137.710938
## 379 132.671875
## 380 103.765625
## 381 94.875000
## 382 129.382812
## 383 121.656250
## 384 112.640625
## 385 114.312500
## 386 120.781250
## 387 96.914062
## 388 111.437500
## 389 95.609375
## 390 42.375000
## 391 23.625000
## 392 115.007812
## 393 113.601562
## 394 96.835938
## 395 109.664062
## 396 98.679688
## 397 111.937500
## 398 109.781250
## 399 118.851562
## 400 101.023438
## 401 118.406250
## 402 108.750000
## 403 123.812500
## 404 107.070312
## 405 111.312500
## 406 102.578125
## 407 131.945312
## 408 127.093750
## 409 50.929688
## 410 92.328125
## 411 100.476562
## 412 130.273438
## 413 111.593750
## 414 120.367188
## 415 95.664062
## 416 113.617188
## 417 111.234375
## 418 142.890625
## 419 117.054688
## 420 111.296875
## 421 108.007812
## 422 114.117188
## 423 116.031250
## 424 126.648438
## 425 138.289062
## 426 86.187500
## 427 133.843750
## 428 118.343750
## 429 147.976562
## 430 82.859375
## 431 97.570312
## 432 130.835938
## 433 112.937500
## 434 107.015625
## 435 107.062500
## 436 122.828125
## 437 137.742188
## 438 130.710938
## 439 129.132812
## 440 86.742188
## 441 121.296875
## 442 138.007812
## 443 116.664062
## 444 129.617188
## 445 90.492188
## 446 101.445312
## 447 108.804688
## 448 96.414062
## 449 118.476562
## 450 106.234375
## 451 145.718750
## 452 105.304688
## 453 103.179688
## 454 126.257812
## 455 115.140625
## 456 119.898438
## 457 109.421875
## 458 122.593750
## 459 96.562500
## 460 90.375000
## 461 124.523438
## 462 133.046875
## 463 131.820312
## 464 120.343750
## 465 130.468750
## 466 113.867188
## 467 95.664062
## 468 76.851562
## 469 133.515625
## 470 104.453125
## 471 74.625000
## 472 72.585938
## 473 124.492188
## 474 116.664062
## 475 127.968750
## 476 101.531250
## 477 121.343750
## 478 136.781250
## 479 116.726562
## 480 118.914062
## 481 143.781250
## 482 89.992188
## 483 129.359375
## 484 124.945312
## 485 133.585938
## 486 108.710938
## 487 110.007812
## 488 116.164062
## 489 121.062500
## 490 146.757812
## 491 135.273438
## 492 114.507812
## 493 137.226562
## 494 118.703125
## 495 125.351562
## 496 92.890625
## 497 137.140625
## 498 128.265625
## 499 114.390625
## 500 90.375000
## 501 134.148438
## 502 139.609375
## 503 125.929688
## 504 109.734375
## 505 91.960938
## 506 96.710938
## 507 113.562500
## 508 6.414062
## 509 136.226562
## 510 121.523438
## 511 79.664062
## 512 130.578125
## 513 141.296875
## 514 91.210938
## 515 97.101562
## 516 95.031250
## 517 109.179688
## 518 75.976562
## 519 120.257812
## 520 130.984375
## 521 120.195312
## 522 116.703125
## 523 85.437500
## 524 87.437500
## 525 117.187500
## 526 152.820312
## 527 118.078125
## 528 112.398438
## 529 121.148438
## 530 109.132812
## 531 122.375000
## 532 163.226562
## 533 120.140625
## 534 145.445312
## 535 117.875000
## 536 126.812500
## 537 154.835938
## 538 111.109375
## 539 151.945312
## 540 52.335938
## 541 121.562500
## 542 133.664062
## 543 84.539062
## 544 102.468750
## 545 133.257812
## 546 110.859375
## 547 125.804688
## 548 90.335938
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## 550 109.437500
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## 553 106.117188
## 554 66.007812
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## 558 124.531250
## 559 137.679688
## 560 25.554688
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## 565 116.890625
## 566 87.679688
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## 570 109.914062
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## 572 118.617188
## 573 43.945312
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## 597 83.890625
## 598 113.390625
## 599 144.835938
## 600 110.812500
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## 603 92.898438
## 604 132.656250
## 605 118.312500
## 606 112.632812
## 607 154.781250
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## 609 98.812500
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## 611 42.351562
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## 613 81.007812
## 614 121.882812
## 615 111.484375
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## 617 122.773438
## 618 112.437500
## 619 52.296875
## 620 105.500000
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## 624 116.875000
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## 627 140.203125
## 628 99.039062
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## 10902 136.265625
## 10903 6.500000
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## 10905 84.757812
## 10906 131.953125
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## 10911 175.328125
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## 10970 111.085938
## 10971 91.929688
## 10972 40.000000
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## 10974 123.250000
## 10975 130.304688
## 10976 65.070312
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## 10984 134.992188
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## 10988 70.125000
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## 11010 12.203125
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## 11012 132.476562
## 11013 103.585938
## 11014 68.867188
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## 11016 45.492188
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## 11018 111.039062
## 11019 131.156250
## 11020 21.882812
## 11021 122.203125
## 11022 130.539062
## 11023 137.437500
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## 11025 82.070312
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## 11027 92.164062
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## 11043 12.476562
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## 11045 114.195312
## 11046 112.132812
## 11047 134.101562
## 11048 134.000000
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## 11052 86.367188
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## 11064 101.031250
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## 11070 77.632812
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## 11082 124.078125
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## 11089 89.921875
## 11090 129.406250
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## 11093 93.367188
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## 11095 25.132812
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## 11097 127.007812
## 11098 150.281250
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## 11100 137.351562
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## 11107 47.203125
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## 11109 123.046875
## 11110 138.882812
## 11111 138.171875
## Standard.deviation.of.the.integrated.profile
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## 10895 43.47703
## 10896 50.98207
## 10897 47.52631
## 10898 46.89503
## 10899 51.90217
## 10900 46.99266
## 10901 52.69960
## 10902 48.75537
## 10903 31.52306
## 10904 48.82698
## 10905 32.61248
## 10906 42.20539
## 10907 53.03658
## 10908 52.98693
## 10909 40.26777
## 10910 54.88020
## 10911 58.16761
## 10912 54.19871
## 10913 54.95577
## 10914 52.30636
## 10915 35.01004
## 10916 43.36394
## 10917 45.39824
## 10918 45.93733
## 10919 56.07065
## 10920 37.65416
## 10921 50.23805
## 10922 42.41084
## 10923 51.04577
## 10924 48.16230
## 10925 37.77201
## 10926 46.86877
## 10927 45.45120
## 10928 54.05328
## 10929 38.03935
## 10930 34.27946
## 10931 39.73215
## 10932 49.51189
## 10933 44.05824
## 10934 46.58903
## 10935 51.45560
## 10936 50.14380
## 10937 35.39657
## 10938 46.57051
## 10939 45.22571
## 10940 62.56834
## 10941 44.98967
## 10942 45.66937
## 10943 55.50675
## 10944 48.37139
## 10945 46.24278
## 10946 48.12020
## 10947 56.95495
## 10948 46.87454
## 10949 41.97621
## 10950 46.90379
## 10951 47.19469
## 10952 38.00199
## 10953 52.12694
## 10954 44.11719
## 10955 50.52133
## 10956 54.63160
## 10957 42.42854
## 10958 52.45448
## 10959 36.09307
## 10960 49.04036
## 10961 34.76422
## 10962 37.98928
## 10963 57.56268
## 10964 47.89296
## 10965 48.66450
## 10966 47.24627
## 10967 46.23465
## 10968 37.96739
## 10969 40.74146
## 10970 50.40756
## 10971 41.04783
## 10972 36.26896
## 10973 51.01123
## 10974 54.32972
## 10975 49.26230
## 10976 47.29366
## 10977 49.74986
## 10978 49.82139
## 10979 54.14714
## 10980 29.96722
## 10981 54.45179
## 10982 53.74110
## 10983 48.29844
## 10984 49.35925
## 10985 46.99699
## 10986 52.44341
## 10987 44.07660
## 10988 37.18325
## 10989 44.10797
## 10990 43.57949
## 10991 37.46185
## 10992 36.36051
## 10993 50.23638
## 10994 58.83106
## 10995 41.15974
## 10996 43.38708
## 10997 47.42626
## 10998 46.83737
## 10999 48.82260
## 11000 40.83770
## 11001 52.97179
## 11002 40.68346
## 11003 53.94160
## 11004 47.15826
## 11005 51.77843
## 11006 47.39914
## 11007 42.00613
## 11008 36.91155
## 11009 43.47378
## 11010 31.21707
## 11011 40.42224
## 11012 52.49285
## 11013 52.34315
## 11014 31.27059
## 11015 48.14983
## 11016 43.49731
## 11017 43.21872
## 11018 51.44086
## 11019 57.98767
## 11020 28.65708
## 11021 43.72559
## 11022 49.65866
## 11023 50.39744
## 11024 42.06140
## 11025 38.53026
## 11026 43.98190
## 11027 38.06138
## 11028 55.92545
## 11029 36.00059
## 11030 45.13573
## 11031 40.65238
## 11032 45.93083
## 11033 45.12700
## 11034 49.95277
## 11035 36.80182
## 11036 36.61186
## 11037 52.28673
## 11038 46.26098
## 11039 44.41312
## 11040 49.26252
## 11041 46.60332
## 11042 30.46527
## 11043 27.75449
## 11044 51.98472
## 11045 44.71648
## 11046 40.79414
## 11047 52.81140
## 11048 51.60578
## 11049 28.96810
## 11050 47.85621
## 11051 58.11078
## 11052 36.47942
## 11053 46.39708
## 11054 54.09018
## 11055 50.46473
## 11056 34.40059
## 11057 41.60791
## 11058 47.73064
## 11059 35.27459
## 11060 55.38769
## 11061 42.60639
## 11062 38.07853
## 11063 35.14509
## 11064 42.49484
## 11065 51.78994
## 11066 53.13345
## 11067 52.08353
## 11068 47.89222
## 11069 52.95872
## 11070 37.17755
## 11071 52.97426
## 11072 50.51744
## 11073 48.10510
## 11074 44.36250
## 11075 35.79744
## 11076 50.66836
## 11077 55.96588
## 11078 45.25873
## 11079 42.00382
## 11080 41.58223
## 11081 40.64340
## 11082 48.81688
## 11083 50.23227
## 11084 34.17481
## 11085 46.30573
## 11086 48.30775
## 11087 37.74423
## 11088 51.17363
## 11089 43.03661
## 11090 50.62445
## 11091 58.13565
## 11092 58.94781
## 11093 34.60014
## 11094 43.45497
## 11095 34.70667
## 11096 53.03707
## 11097 49.70546
## 11098 57.23389
## 11099 49.47516
## 11100 51.75507
## 11101 52.59065
## 11102 50.85053
## 11103 37.06431
## 11104 54.80120
## 11105 47.06040
## 11106 45.62450
## 11107 61.00824
## 11108 47.51269
## 11109 54.23947
## 11110 43.25065
## 11111 41.69441
## Excess.kurtosis.of.the.integrated.profile
## 1 0.375484665
## 2 0.712897860
## 3 0.133408289
## 4 -0.215988596
## 5 0.825012787
## 6 0.229708131
## 7 1.063412906
## 8 0.565105947
## 9 0.347578100
## 10 0.394181838
## 11 -0.046584831
## 12 0.419405572
## 13 -0.468418783
## 14 -0.091204187
## 15 0.329515230
## 16 0.109210844
## 17 -0.256682808
## 18 0.350505360
## 19 0.120403695
## 20 0.012855051
## 21 0.114333106
## 22 0.765557849
## 23 0.748776820
## 24 -0.155980676
## 25 0.272860629
## 26 -0.028664610
## 27 0.216742660
## 28 0.462444242
## 29 0.058376664
## 30 0.543618819
## 31 0.349156883
## 32 4.363306569
## 33 0.233883385
## 34 0.214801403
## 35 4.334565003
## 36 0.017993670
## 37 0.013886214
## 38 -0.225395534
## 39 0.903602920
## 40 0.062489168
## 41 1.802132263
## 42 0.499828032
## 43 -0.101259289
## 44 0.512676158
## 45 -0.343295424
## 46 0.421809082
## 47 0.028076659
## 48 -0.049487341
## 49 0.895555863
## 50 0.314973781
## 51 0.296745946
## 52 0.162014348
## 53 0.064627218
## 54 0.534375121
## 55 0.238169773
## 56 0.299161121
## 57 0.129595466
## 58 0.135934885
## 59 -0.126128261
## 60 0.088329184
## 61 0.217252967
## 62 -0.218267353
## 63 0.394053112
## 64 -0.002738310
## 65 0.403119745
## 66 0.566614165
## 67 0.207246515
## 68 0.148266465
## 69 1.093096134
## 70 -0.121200047
## 71 0.685814307
## 72 -0.080527561
## 73 0.182795197
## 74 0.380317761
## 75 0.116486330
## 76 -0.108416941
## 77 0.069834842
## 78 -0.247884078
## 79 0.173898954
## 80 0.315869601
## 81 0.411826466
## 82 0.842887995
## 83 1.584939782
## 84 -0.077511417
## 85 0.372519663
## 86 0.192329365
## 87 0.154800624
## 88 0.007801765
## 89 0.282037699
## 90 -0.021829744
## 91 0.084456105
## 92 -0.153433889
## 93 0.211699841
## 94 0.238332347
## 95 0.078452945
## 96 -0.419232164
## 97 0.331789895
## 98 0.342410251
## 99 -0.466381765
## 100 0.667673695
## 101 0.733140039
## 102 0.022502268
## 103 -0.065194501
## 104 0.295059112
## 105 -0.280868669
## 106 0.045658940
## 107 -0.045039016
## 108 0.395521947
## 109 0.495675632
## 110 0.300662823
## 111 0.456108308
## 112 0.090649498
## 113 0.436000412
## 114 0.650115394
## 115 0.807330789
## 116 -0.181519541
## 117 0.314228254
## 118 -0.048904984
## 119 0.668382061
## 120 0.052261198
## 121 0.101663708
## 122 0.803473542
## 123 2.078866290
## 124 0.110689295
## 125 0.258934957
## 126 0.595646138
## 127 0.208894980
## 128 0.028392948
## 129 -0.032284824
## 130 0.225556765
## 131 0.108284036
## 132 0.173075914
## 133 2.265220701
## 134 -0.225800599
## 135 -0.111128087
## 136 -0.352966276
## 137 0.042272873
## 138 0.340431133
## 139 -0.312699930
## 140 0.302312855
## 141 -0.191159541
## 142 0.129922059
## 143 1.076262683
## 144 0.119318565
## 145 0.346220816
## 146 -0.095710749
## 147 -0.005444107
## 148 0.269434624
## 149 -0.157853185
## 150 -0.065190361
## 151 0.003367340
## 152 0.323824914
## 153 0.450477233
## 154 0.001044529
## 155 0.373326109
## 156 -0.032366243
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## 158 0.220206067
## 159 0.194971812
## 160 7.088609518
## 161 0.680613625
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## 165 0.419055129
## 166 -0.232863347
## 167 -0.018332218
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## 170 0.273396446
## 171 0.178546798
## 172 0.228534597
## 173 0.516903619
## 174 0.375889995
## 175 0.008293758
## 176 0.132766391
## 177 0.112007491
## 178 0.273500764
## 179 2.542730429
## 180 0.428837305
## 181 0.580524256
## 182 0.185130893
## 183 -0.158676799
## 184 -0.221792439
## 185 0.742338219
## 186 -0.193884989
## 187 0.176057944
## 188 0.079585207
## 189 -0.133282211
## 190 3.736450963
## 191 0.049738567
## 192 -0.211585074
## 193 -0.204780636
## 194 0.275964648
## 195 0.611750363
## 196 0.137360862
## 197 0.576817027
## 198 1.068642230
## 199 2.812397389
## 200 0.103964730
## 201 0.468680550
## 202 0.224752702
## 203 0.012559794
## 204 0.314458878
## 205 -0.205287269
## 206 0.163213636
## 207 0.170699268
## 208 0.027884059
## 209 0.556903760
## 210 0.559001978
## 211 0.131189038
## 212 -0.126608942
## 213 0.333484912
## 214 -0.163717546
## 215 0.333211478
## 216 0.470331606
## 217 0.029280051
## 218 0.309801971
## 219 0.493948894
## 220 0.127127492
## 221 0.091929594
## 222 0.636330543
## 223 0.610609396
## 224 -0.182433321
## 225 0.083300036
## 226 1.674993962
## 227 -0.134165552
## 228 3.552223686
## 229 -0.117197090
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## 231 0.486719086
## 232 -0.037057730
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## 235 -0.056210836
## 236 0.622558912
## 237 0.501205706
## 238 -0.152055718
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## 240 1.800950705
## 241 -0.111208573
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## 243 0.479493506
## 244 0.186128622
## 245 0.245544714
## 246 0.075312344
## 247 3.478736350
## 248 0.616439043
## 249 0.127531553
## 250 0.170221538
## 251 -0.199381208
## 252 0.106865405
## 253 -0.531144854
## 254 0.228848180
## 255 -0.058233719
## 256 4.153427856
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## 259 -0.007886563
## 260 -0.213224648
## 261 0.097734271
## 262 -0.155516985
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## 284 -0.067437794
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## 287 5.502086902
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## 290 -0.117206658
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## 292 -0.023317526
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## 295 4.635340378
## 296 0.073884697
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## 300 -0.296626839
## 301 -0.057378358
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## 303 0.072077648
## 304 0.353903150
## 305 2.982096870
## 306 0.909367960
## 307 0.480249282
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## 309 0.024982345
## 310 0.462361504
## 311 0.093161028
## 312 0.040561635
## 313 0.260014525
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## 415 0.406162834
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## 10979 -0.086338356
## 10980 4.107268843
## 10981 0.046120647
## 10982 0.655981731
## 10983 -0.095044453
## 10984 0.152727830
## 10985 0.598654819
## 10986 -0.273670204
## 10987 -0.193667950
## 10988 2.219011921
## 10989 0.087700320
## 10990 -0.016918254
## 10991 0.803640460
## 10992 4.360465712
## 10993 -0.108407305
## 10994 0.290841570
## 10995 0.266261012
## 10996 0.431059613
## 10997 0.030250946
## 10998 0.434540871
## 10999 0.201694026
## 11000 0.667646326
## 11001 0.105704505
## 11002 3.490423980
## 11003 0.257401847
## 11004 0.619954867
## 11005 0.262187455
## 11006 0.175273797
## 11007 0.311358029
## 11008 0.414356343
## 11009 0.593664420
## 11010 5.926254772
## 11011 0.191177280
## 11012 -0.112397665
## 11013 0.245138956
## 11014 1.606918770
## 11015 0.084616180
## 11016 3.033387283
## 11017 0.318156187
## 11018 0.416725544
## 11019 -0.091661970
## 11020 6.076240301
## 11021 0.058498931
## 11022 -0.117926553
## 11023 -0.797783316
## 11024 2.257081435
## 11025 0.860951268
## 11026 -0.157884567
## 11027 0.340268490
## 11028 0.342951980
## 11029 0.350439473
## 11030 0.542460917
## 11031 -0.444932338
## 11032 0.242612143
## 11033 0.252687947
## 11034 0.111526286
## 11035 0.683694235
## 11036 0.518054468
## 11037 0.086861429
## 11038 0.057837081
## 11039 0.158140516
## 11040 0.394893807
## 11041 -0.098536494
## 11042 6.166521494
## 11043 7.037477937
## 11044 0.081616361
## 11045 0.226155438
## 11046 0.177570386
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## 11048 0.074005503
## 11049 7.383228067
## 11050 -0.646255394
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## 11052 0.851337465
## 11053 0.298616537
## 11054 0.006562854
## 11055 -0.106103588
## 11056 0.643909640
## 11057 0.729465117
## 11058 0.217747786
## 11059 2.933662904
## 11060 0.089727994
## 11061 0.570976306
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## 11063 3.392913848
## 11064 0.312657897
## 11065 -0.084586046
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## 11067 -0.379283904
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## 11075 0.561321992
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## 11077 -0.399576605
## 11078 0.547538909
## 11079 1.321354234
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## 11082 -0.069257799
## 11083 -0.113177563
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## 11090 -0.082171352
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## 11101 0.249983111
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## 11107 2.081977286
## 11108 -0.215999150
## 11109 -0.113120482
## 11110 -0.189525873
## 11111 -0.071785887
## Skewness.of.the.integrated.profile Mean.of.the.DM.SNR.curve
## 1 -0.013165489 3.1688963
## 2 3.388718563 2.3996656
## 3 -0.297241641 2.7433110
## 4 -0.171293649 17.4715719
## 5 3.274125373 2.7901338
## 6 0.091336230 2.0367893
## 7 2.244376690 141.6413043
## 8 0.056246664 2.7976589
## 9 1.153164381 2.7700669
## 10 0.190295686 4.5785953
## 11 -0.034468391 5.0501672
## 12 0.755829381 1.9715719
## 13 0.554926309 2.8854515
## 14 0.307802940 5.3076923
## 15 0.180464824 1.9188963
## 16 -0.413431636 2.2466555
## 17 -0.594705880 137.1488294
## 18 0.165345961 0.4732441
## 19 -0.518847463 3.5652174
## 20 -0.804890239 31.5535117
## 21 -0.041770772 3.2182274
## 22 -0.827640030 197.2341137
## 23 1.342901713 1.7474916
## 24 0.375566660 5.2299331
## 25 -0.061646003 2.1396321
## 26 -0.206532229 3.9556856
## 27 -0.291253721 9.3720736
## 28 0.245138892 17.3795987
## 29 -0.495919613 1.7566890
## 30 0.455562472 4.7508361
## 31 0.853318645 1.8152174
## 32 20.296980080 107.2123746
## 33 -0.011839737 3.4289298
## 34 -0.135503290 2.7316054
## 35 26.339869770 39.5852843
## 36 -0.417075863 1.7968227
## 37 -0.711808530 3.2190635
## 38 0.390774775 2.5986622
## 39 2.969175667 1.1964883
## 40 0.431275602 2.3804348
## 41 2.232983407 67.0209030
## 42 0.060150662 4.1086957
## 43 -0.398289737 2.7056856
## 44 0.450317547 2.4188963
## 45 0.391281115 7.1153846
## 46 1.514337381 1.3168896
## 47 -0.176735487 2.4322742
## 48 0.130211339 3.6479933
## 49 3.164132145 1.8503344
## 50 0.949607938 1.6112040
## 51 0.341512169 1.0777592
## 52 -0.400657998 1.8327759
## 53 -0.596634779 2.8578595
## 54 1.450842937 3.6061873
## 55 0.266654812 2.2190635
## 56 -0.232141931 3.5317726
## 57 -0.126275519 2.3118729
## 58 -0.313794504 5.4180602
## 59 -0.524239396 8.8837793
## 60 -0.147103385 1.4615385
## 61 -0.367926062 1.3586957
## 62 0.014367744 5.1705686
## 63 0.036605939 0.7583612
## 64 0.297345480 5.7182274
## 65 1.648295446 2.1036789
## 66 1.158834437 2.5744147
## 67 -0.188130235 1.1304348
## 68 -0.804809152 2.0543478
## 69 4.435121802 2.3453177
## 70 -0.361874096 3.1705686
## 71 0.547892636 2.2658863
## 72 0.646575786 2.0693980
## 73 0.128979534 2.3595318
## 74 0.308553175 4.9832776
## 75 -0.277562950 16.8963211
## 76 1.299277285 17.4782609
## 77 -0.443164315 1.8469900
## 78 0.098468574 4.2583612
## 79 -0.429864195 1.1998328
## 80 1.802161665 2.9506689
## 81 0.503116060 4.8904682
## 82 1.583587725 56.2031773
## 83 3.885098004 34.2173913
## 84 0.272260622 4.2650502
## 85 1.437456043 3.4138796
## 86 0.406702378 2.5551839
## 87 0.017427097 1.1588629
## 88 0.361931836 3.5785953
## 89 0.117742368 2.2767559
## 90 0.367626019 4.8729097
## 91 1.379678654 14.4122074
## 92 -0.682348594 3.1872910
## 93 0.865675377 1.4757525
## 94 -0.010529437 0.9607023
## 95 0.533665705 1.9096990
## 96 -0.493051215 2.1588629
## 97 -0.478993736 1.5000000
## 98 0.091198089 1.6413043
## 99 0.029699801 2.6371237
## 100 1.086732511 0.7759197
## 101 3.108239721 2.0451505
## 102 -0.123905232 2.7408027
## 103 1.335937209 1.4489967
## 104 0.013024589 15.7399666
## 105 -1.169241732 2.6839465
## 106 -0.160817322 2.6521739
## 107 0.151413844 2.5961538
## 108 0.469878693 1.3762542
## 109 0.529467661 1.0392977
## 110 -0.008949430 4.3394649
## 111 0.823766476 1.9899666
## 112 -0.147498257 1.3720736
## 113 1.731325660 2.1739130
## 114 0.970000225 1.4640468
## 115 2.174818818 1.8687291
## 116 -0.029142406 0.5016722
## 117 -0.710808631 1.1446488
## 118 0.499820383 0.5434783
## 119 1.695142493 2.0710702
## 120 0.144511739 4.7190635
## 121 -0.087821480 8.3929766
## 122 4.018279676 2.3645485
## 123 4.677566913 99.6981605
## 124 1.223632929 3.0610368
## 125 0.324588769 1.5953177
## 126 3.899363677 3.2600334
## 127 0.820881228 3.0117057
## 128 -0.274334322 4.1103679
## 129 -0.337968550 1.3545151
## 130 -0.067658364 1.3670569
## 131 -0.212734338 3.1454850
## 132 0.175957503 1.9949833
## 133 8.709980861 5.5936455
## 134 0.485866888 23.8168896
## 135 -0.096114076 15.6513378
## 136 -0.268633830 5.9155518
## 137 0.392180358 2.5484950
## 138 0.603376608 0.7182274
## 139 -0.405669597 19.8503344
## 140 0.515795646 6.6596990
## 141 -0.565564884 2.7265886
## 142 -0.150216420 1.8453177
## 143 3.581447394 1.0142140
## 144 -0.360250434 1.9230769
## 145 -0.705472728 3.7775920
## 146 -0.558537431 4.7625418
## 147 0.279033174 103.0936455
## 148 -0.261158229 2.6722408
## 149 -0.698161438 1.9866221
## 150 -0.180708288 2.6797659
## 151 -0.312988521 1.2148829
## 152 0.130624868 1.9038462
## 153 1.557485679 3.0811037
## 154 -0.114471999 1.8988294
## 155 1.711139757 2.1780936
## 156 -0.119016489 2.5568562
## 157 -0.366704922 175.5785953
## 158 0.055751965 2.0702341
## 159 0.195138691 31.5760870
## 160 50.954249040 84.1170569
## 161 1.188921894 4.3503344
## 162 0.730957013 1.7391304
## 163 0.011775402 2.3979933
## 164 0.061427247 2.0802676
## 165 0.119018599 1.7934783
## 166 -0.102144782 3.7541806
## 167 0.002608603 10.4021739
## 168 1.982141691 11.5334448
## 169 1.814031082 3.3202341
## 170 0.741843588 1.6220736
## 171 0.769809411 2.5652174
## 172 -0.465533862 2.2993311
## 173 0.500946310 63.1672241
## 174 1.353086120 1.5484950
## 175 -0.141872468 2.4866221
## 176 1.049137968 4.2165552
## 177 0.144351009 2.2483278
## 178 -0.321400647 27.0576923
## 179 10.994893050 7.8344482
## 180 0.234800176 2.0158863
## 181 1.452630674 1.6212375
## 182 -0.528603896 12.1488294
## 183 -0.588693512 1.8528428
## 184 -0.309404146 2.3754181
## 185 1.309664351 25.0744147
## 186 0.190579156 1.1914716
## 187 0.782359685 3.9021739
## 188 -0.288818016 119.0326087
## 189 -0.132767876 3.6998328
## 190 14.914644700 111.1229097
## 191 0.126347724 0.8871237
## 192 -0.295598653 4.0351171
## 193 -0.122476171 111.0275920
## 194 -0.224435364 4.9038462
## 195 1.617375621 1.9774247
## 196 1.231334637 2.0869565
## 197 0.098896545 147.1856187
## 198 2.953030566 1.4356187
## 199 8.285401673 2.4205686
## 200 0.553664436 102.9214047
## 201 -0.479538943 2.5869565
## 202 0.386367320 9.8570234
## 203 -0.286798828 3.2591973
## 204 0.886810107 1.6839465
## 205 -0.226105863 49.8946488
## 206 -0.787813653 142.1856187
## 207 -0.072645839 2.2683946
## 208 0.149187376 1.1981605
## 209 0.545368928 1.4013378
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## 211 0.212466386 2.2441472
## 212 0.059228296 3.9991639
## 213 -0.514904640 2.7633779
## 214 -0.234824956 1.6697324
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## 216 0.462411635 2.5108696
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## 218 -0.471439018 8.1505017
## 219 1.442413744 1.5535117
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## 221 -0.260848326 2.2742475
## 222 0.924778828 2.1254181
## 223 1.245056491 2.2056856
## 224 -0.201564654 2.3377926
## 225 0.447279133 1.8093645
## 226 3.460698399 59.1195652
## 227 0.003438096 4.5568562
## 228 20.283088680 12.7299331
## 229 -0.289606488 1.6989967
## 230 0.862941112 1.7173913
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## 236 0.777562644 3.3779264
## 237 0.413929451 5.7993311
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## 239 4.156706332 7.3327759
## 240 5.818561928 4.8352843
## 241 -0.402939199 1.9021739
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## 243 0.559258850 5.7324415
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## 246 -0.433520599 2.1254181
## 247 12.324361720 101.5953177
## 248 0.696161157 2.0543478
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## 250 -0.610939454 2.0585284
## 251 -0.362322805 1.6212375
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## 253 -0.571877226 75.0259197
## 254 1.096165284 3.1312709
## 255 0.123057192 2.2809365
## 256 17.813680220 141.4607023
## 257 2.630586370 3.4615385
## 258 -0.476816314 1.7274247
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## 261 -0.103949405 2.6747492
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## 264 1.566673172 1.3779264
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## 266 0.009136227 2.4289298
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## 269 -0.011905415 132.4147157
## 270 -0.377985873 1.8988294
## 271 27.665741620 63.0175585
## 272 1.871258295 1.7575251
## 273 -0.419988282 3.2692308
## 274 1.105027547 2.3010033
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## 283 24.486690600 34.1011706
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## 285 5.535417171 15.1797659
## 286 0.988715347 1.1396321
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## 288 -0.406463260 3.8344482
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## 1959 0.702528822 1.1112040
## 1960 0.135073354 23.9431438
## 1961 -0.027493763 0.4222408
## 1962 3.889400357 14.2959866
## 1963 20.312966390 61.2675585
## 1964 -0.262420331 119.7399666
## 1965 20.814968210 32.8302676
## 1966 -0.976845205 2.2215719
## 1967 4.546692371 3.0033445
## 1968 4.555957337 10.1279264
## 1969 6.693899684 9.3219064
## 1970 1.482347652 1.5317726
## 1971 1.004083947 2.1187291
## 1972 -0.458478480 2.1989967
## 1973 0.484780376 1.7525084
## 1974 -0.105485044 2.9548495
## 1975 1.186277774 0.7157191
## 1976 0.626551326 2.1939799
## 1977 0.067623561 4.3110368
## 1978 -0.220176997 2.7015050
## 1979 -0.071931153 2.2767559
## 1980 -0.586280640 1.4832776
## 1981 19.397212600 64.1362876
## 1982 -0.392687667 3.0827759
## 1983 0.149071340 2.2048495
## 1984 1.431355729 1.8227425
## 1985 0.453594631 3.1153846
## 1986 -0.558991494 7.0844482
## 1987 32.826426940 94.1948161
## 1988 -0.005414777 0.6889632
## 1989 5.957139157 74.1429766
## 1990 -0.265610249 2.2566890
## 1991 8.714217534 4.1421405
## 1992 0.107515456 3.5510033
## 1993 0.711608265 4.4230769
## 1994 2.178786108 3.4924749
## 1995 0.485451518 2.2683946
## 1996 -0.639172843 2.1688963
## 1997 0.732046294 2.0652174
## 1998 1.602988763 37.7658863
## 1999 0.360270494 1.9857860
## 2000 9.433251250 8.0008361
## 2001 0.723430264 1.7249164
## 2002 -0.299438676 2.4857860
## 2003 0.791787019 2.1806020
## 2004 33.156219620 54.6170569
## 2005 3.691190181 1.8269231
## 2006 0.297925564 2.9707358
## 2007 1.706842028 4.6939799
## 2008 3.242144059 2.2550167
## 2009 0.247931324 2.8879599
## 2010 -0.167484869 1.9941472
## 2011 16.534912750 129.3336120
## 2012 0.321431953 5.0367893
## 2013 0.796065675 3.3704013
## 2014 0.122473997 123.4406355
## 2015 0.246250647 3.4372910
## 2016 -0.286011017 5.7382943
## 2017 -0.217467528 8.5543478
## 2018 1.301647917 1.2073579
## 2019 -0.648617371 2.4899666
## 2020 -0.646461564 2.8453177
## 2021 -0.271453424 1.6638796
## 2022 0.018195006 0.7566890
## 2023 0.047956005 1.6421405
## 2024 1.087703773 1.9230769
## 2025 0.081202847 1.2349498
## 2026 -0.315938907 11.2909699
## 2027 0.067345618 3.1220736
## 2028 0.577109029 1.5209030
## 2029 0.102127572 1.5518395
## 2030 -0.095925527 32.0192308
## 2031 0.643539961 169.0484950
## 2032 -0.593419574 3.7617057
## 2033 0.123149725 2.9799331
## 2034 -0.807788259 4.7391304
## 2035 -0.222835792 3.3001672
## 2036 -0.329961140 20.5133779
## 2037 -0.109980092 1.7784281
## 2038 0.011623829 1.9774247
## 2039 -0.007450342 2.5836120
## 2040 1.121193488 0.6739130
## 2041 2.442330219 1.1270903
## 2042 0.395791052 1.0785953
## 2043 -0.481814249 2.5309365
## 2044 1.343421367 2.7224080
## 2045 -0.580171595 2.3219064
## 2046 0.598908382 1.3478261
## 2047 0.006931417 2.3645485
## 2048 27.611794290 59.4607023
## 2049 -0.749947946 2.1923077
## 2050 1.140288415 10.7525084
## 2051 0.118414176 6.3494983
## 2052 0.259115055 4.4038462
## 2053 -0.510287132 6.6379599
## 2054 0.903789591 2.5284281
## 2055 -0.077064869 0.7157191
## 2056 -0.631225444 1.8754181
## 2057 48.339853750 91.2901338
## 2058 0.258854879 108.9774247
## 2059 0.166567400 1.4715719
## 2060 1.189597852 1.4414716
## 2061 -0.310645080 5.8160535
## 2062 0.077817506 3.0602007
## 2063 -0.045095038 60.0426421
## 2064 0.073556667 2.0066890
## 2065 0.883343133 2.2249164
## 2066 0.271642564 1.5844482
## 2067 -0.037088762 0.3177258
## 2068 -0.211016882 5.2959866
## 2069 1.065385728 2.6622074
## 2070 0.799565334 2.1881271
## 2071 0.226865865 2.3453177
## 2072 -0.290893211 2.8085284
## 2073 -0.249694466 14.4188963
## 2074 14.998360070 110.2357860
## 2075 -0.193731295 188.2959866
## 2076 17.227460740 61.2132107
## 2077 0.564448044 4.2065217
## 2078 -0.734329989 2.0836120
## 2079 -0.120299110 5.7173913
## 2080 -0.190084452 48.8787625
## 2081 -0.336557474 1.9816054
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## 2083 0.298789366 1.6989967
## 2084 -0.328961814 4.0100334
## 2085 0.258925947 3.5459866
## 2086 -0.071052486 0.8361204
## 2087 2.026089747 41.2324415
## 2088 -0.268227219 3.3277592
## 2089 -0.146276763 9.0091973
## 2090 0.665457146 1.3628763
## 2091 -0.390939566 2.3051839
## 2092 0.948051818 2.2441472
## 2093 -0.079589893 1.2583612
## 2094 9.184122277 22.6329431
## 2095 -0.374934811 6.3311037
## 2096 0.508382275 6.4849498
## 2097 -0.207810348 1.6371237
## 2098 1.424404830 1.1822742
## 2099 1.031761317 2.3511706
## 2100 -0.382127219 1.1989967
## 2101 1.533002530 1.0301003
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## 2136 0.125849601 0.6505017
## 2137 32.106542850 31.5903010
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## 10987 0.091355993 2.6371237
## 10988 7.845536994 62.5125418
## 10989 0.182653600 2.5919732
## 10990 0.153170516 4.1663880
## 10991 1.959775213 2.2098662
## 10992 21.157711570 15.3687291
## 10993 -0.431259477 2.3285953
## 10994 -0.622312321 0.8227425
## 10995 1.166449844 2.1187291
## 10996 0.866741155 1.2633779
## 10997 0.021707592 4.0785953
## 10998 0.548485294 5.5693980
## 10999 0.122594589 2.7826087
## 11000 1.629132225 2.7951505
## 11001 -0.481420548 6.7943144
## 11002 12.541821480 50.2165552
## 11003 -0.273153528 1.6354515
## 11004 0.576317466 1.8946488
## 11005 0.209847633 1.8545151
## 11006 0.173889313 0.7600334
## 11007 0.391926512 1.5451505
## 11008 2.453588200 2.8904682
## 11009 1.095129761 3.3068562
## 11010 36.850035100 98.8678930
## 11011 0.485737066 2.5568562
## 11012 -0.293257982 1.4816054
## 11013 -0.385240035 3.0016722
## 11014 8.278341954 3.6864549
## 11015 0.027177723 4.3202341
## 11016 9.431701660 46.8035117
## 11017 0.102120850 2.3361204
## 11018 -0.204982108 2.2433110
## 11019 -0.642411308 0.6471572
## 11020 40.764296750 75.9841137
## 11021 0.350248208 2.0225753
## 11022 -0.096793790 4.0025084
## 11023 -0.047807418 5.4648829
## 11024 6.567822209 141.0217391
## 11025 2.396808662 0.5944816
## 11026 0.254012473 3.6371237
## 11027 1.531934364 4.3653846
## 11028 -0.244647355 7.2249164
## 11029 2.296346993 1.8662207
## 11030 1.406067298 4.6178930
## 11031 0.635781490 0.3720736
## 11032 0.130072578 3.2366221
## 11033 0.324622516 1.5158863
## 11034 -0.084342636 1.6295987
## 11035 2.280964158 3.9230769
## 11036 2.127668173 1.1847826
## 11037 -0.395844103 0.6630435
## 11038 0.244327803 2.9640468
## 11039 0.164625477 4.1304348
## 11040 -0.033904739 6.3812709
## 11041 0.089892053 6.1897993
## 11042 39.912909380 63.0568562
## 11043 52.458596400 130.2658863
## 11044 -0.280946202 2.0016722
## 11045 0.494021797 3.3503344
## 11046 0.516114454 4.1864548
## 11047 -0.344997980 2.1287625
## 11048 -0.298437574 3.4565217
## 11049 54.914505810 77.3578595
## 11050 0.426753852 66.9122074
## 11051 -0.750325135 2.8294314
## 11052 2.353828928 4.3469900
## 11053 0.345105750 0.7249164
## 11054 -0.533371047 1.8520067
## 11055 -0.113129133 3.6011706
## 11056 2.690050842 1.6354515
## 11057 1.211588181 1.8979933
## 11058 -0.263104974 1.6530100
## 11059 13.233200670 43.0610368
## 11060 -0.330473503 1.9280936
## 11061 0.896481088 1.3553512
## 11062 1.115946140 1.4481605
## 11063 14.633296980 24.8570234
## 11064 1.441154860 1.2767559
## 11065 -0.514806073 28.4147157
## 11066 -0.312716464 6.4172241
## 11067 -0.180738702 0.6513378
## 11068 -0.227866611 1.9799331
## 11069 -0.166284785 10.6989967
## 11070 2.811337943 2.5209030
## 11071 -0.423986480 27.0041806
## 11072 -0.404632628 2.5158863
## 11073 -0.138890023 14.0978261
## 11074 1.329284173 2.3887960
## 11075 2.620863648 2.6630435
## 11076 -0.418057563 1.0593645
## 11077 -0.435533217 1.7157191
## 11078 0.574392041 3.4974916
## 11079 4.153582856 11.7709030
## 11080 10.138266090 159.6112040
## 11081 0.979402331 2.1956522
## 11082 -0.122427748 3.1881271
## 11083 -0.210180996 3.2876254
## 11084 19.611846420 21.8202341
## 11085 0.076051409 3.1162207
## 11086 0.296793264 2.2090301
## 11087 1.359279387 37.0677258
## 11088 -0.463465295 1.6613712
## 11089 0.819435257 1.8461538
## 11090 -0.260305910 8.1755853
## 11091 -0.498638749 3.2324415
## 11092 -0.670209223 2.2215719
## 11093 2.676841513 3.0451505
## 11094 0.255620368 7.7959866
## 11095 25.225663060 91.4046823
## 11096 -0.422421244 1.6697324
## 11097 -0.042894302 2.6605351
## 11098 0.090977919 119.8327759
## 11099 -0.022998697 7.9866221
## 11100 -0.323477630 1.7382943
## 11101 -0.018480168 2.4657191
## 11102 0.152017114 1.5459866
## 11103 11.865938430 69.3737458
## 11104 -0.446406142 1.7650502
## 11105 0.157056660 2.0175585
## 11106 0.727714998 2.6070234
## 11107 2.740597233 23.1923077
## 11108 0.190107099 11.2993311
## 11109 -0.465978679 2.3879599
## 11110 0.373259041 2.3428094
## 11111 0.438525884 2.6245819
## Standard.deviation.of.the.DM.SNR.curve
## 1 18.399367
## 2 17.570997
## 3 22.362553
## 4 46.546463
## 5 20.618009
## 6 18.302704
## 7 56.764702
## 8 19.496527
## 9 18.217741
## 10 28.590601
## 11 27.997476
## 12 11.636964
## 13 18.043781
## 14 25.162295
## 15 14.846797
## 16 13.538692
## 17 51.223456
## 18 9.305757
## 19 22.372342
## 20 65.236254
## 21 17.953117
## 22 63.758798
## 23 12.070575
## 24 23.907751
## 25 16.655713
## 26 26.251782
## 27 35.106210
## 28 51.981744
## 29 16.897443
## 30 28.102264
## 31 13.492664
## 32 74.621195
## 33 22.805726
## 34 21.993288
## 35 75.533411
## 36 13.684562
## 37 19.526429
## 38 17.145047
## 39 12.178948
## 40 11.422014
## 41 80.612872
## 42 21.705768
## 43 14.418100
## 44 17.697739
## 45 32.017450
## 46 11.166979
## 47 13.379198
## 48 20.200972
## 49 15.446320
## 50 13.439267
## 51 12.534160
## 52 17.559706
## 53 18.310218
## 54 21.245805
## 55 13.922402
## 56 20.847988
## 57 18.861285
## 58 29.236937
## 59 41.558566
## 60 11.595511
## 61 12.478514
## 62 27.958809
## 63 9.734999
## 64 25.782504
## 65 18.008247
## 66 15.407247
## 67 12.892651
## 68 14.649571
## 69 20.200260
## 70 18.300657
## 71 13.184802
## 72 14.735219
## 73 14.671184
## 74 28.880935
## 75 51.416451
## 76 46.029876
## 77 13.241170
## 78 21.325499
## 79 14.824538
## 80 20.500185
## 81 23.738871
## 82 75.998733
## 83 64.417648
## 84 24.599824
## 85 21.925118
## 86 17.639870
## 87 10.661790
## 88 23.389597
## 89 14.457046
## 90 21.559467
## 91 53.979417
## 92 16.531263
## 93 14.318063
## 94 10.062395
## 95 15.921585
## 96 20.614227
## 97 13.988379
## 98 11.309980
## 99 16.335388
## 100 10.155807
## 101 15.170865
## 102 15.082927
## 103 13.932706
## 104 45.046614
## 105 21.101374
## 106 20.428546
## 107 15.989648
## 108 12.789480
## 109 13.650840
## 110 24.277282
## 111 17.799184
## 112 10.583378
## 113 14.635820
## 114 13.155344
## 115 18.199066
## 116 18.270864
## 117 13.876606
## 118 10.081306
## 119 16.336475
## 120 26.456432
## 121 35.662123
## 122 16.368945
## 123 79.813862
## 124 17.803426
## 125 11.598283
## 126 20.941193
## 127 24.447812
## 128 24.020327
## 129 10.435648
## 130 14.850646
## 131 16.517718
## 132 15.986720
## 133 29.623960
## 134 62.888810
## 135 49.303151
## 136 28.217198
## 137 16.650554
## 138 9.763681
## 139 54.987398
## 140 32.016725
## 141 19.953198
## 142 17.264768
## 143 14.439409
## 144 12.255806
## 145 26.556163
## 146 25.797069
## 147 93.269145
## 148 17.737955
## 149 16.583724
## 150 16.473967
## 151 12.032974
## 152 12.742167
## 153 14.596307
## 154 12.277272
## 155 15.668113
## 156 14.591687
## 157 88.165423
## 158 14.823773
## 159 59.544909
## 160 59.367088
## 161 25.984531
## 162 12.567830
## 163 14.626754
## 164 13.339605
## 165 15.854899
## 166 24.897242
## 167 39.759242
## 168 43.870855
## 169 21.144280
## 170 11.825698
## 171 16.541574
## 172 20.360440
## 173 85.327338
## 174 8.588882
## 175 15.340254
## 176 25.951313
## 177 13.786695
## 178 69.247345
## 179 33.661621
## 180 14.375527
## 181 13.582136
## 182 40.803935
## 183 17.814168
## 184 19.157646
## 185 62.868452
## 186 9.935543
## 187 21.111496
## 188 85.955906
## 189 21.090766
## 190 75.716494
## 191 10.244901
## 192 26.521502
## 193 77.892539
## 194 30.644925
## 195 14.760813
## 196 13.376931
## 197 75.296020
## 198 11.386860
## 199 17.796496
## 200 66.957161
## 201 18.540247
## 202 37.191097
## 203 18.645066
## 204 13.046563
## 205 80.562491
## 206 82.339541
## 207 16.476264
## 208 15.089589
## 209 14.527893
## 210 12.820514
## 211 15.661093
## 212 24.239348
## 213 16.028665
## 214 14.222331
## 215 12.064034
## 216 17.375489
## 217 11.212456
## 218 30.499817
## 219 16.921607
## 220 12.440749
## 221 15.238627
## 222 19.696000
## 223 14.058865
## 224 16.657316
## 225 11.767556
## 226 53.954117
## 227 27.775424
## 228 43.221350
## 229 16.746578
## 230 11.899400
## 231 10.519512
## 232 25.448234
## 233 25.058461
## 234 11.910918
## 235 15.190521
## 236 25.690921
## 237 30.816369
## 238 12.861982
## 239 33.815053
## 240 26.455088
## 241 9.924385
## 242 20.539103
## 243 30.629712
## 244 15.660941
## 245 14.677691
## 246 16.870230
## 247 83.389845
## 248 13.711476
## 249 22.675974
## 250 14.667608
## 251 15.238884
## 252 14.320316
## 253 73.552749
## 254 22.009925
## 255 16.450839
## 256 62.660371
## 257 20.118861
## 258 13.047224
## 259 18.284811
## 260 15.871933
## 261 21.919328
## 262 16.820311
## 263 49.932114
## 264 10.696137
## 265 16.528740
## 266 16.055010
## 267 57.439944
## 268 22.134617
## 269 65.067503
## 270 18.131367
## 271 65.773572
## 272 13.745102
## 273 15.870337
## 274 20.496177
## 275 17.595535
## 276 15.481005
## 277 22.062204
## 278 40.575989
## 279 15.356027
## 280 22.185239
## 281 17.922255
## 282 15.199537
## 283 62.577395
## 284 15.496355
## 285 50.887790
## 286 12.141298
## 287 72.055583
## 288 20.810123
## 289 15.471363
## 290 15.891647
## 291 9.488659
## 292 15.854899
## 293 32.399087
## 294 16.065176
## 295 54.812806
## 296 17.720014
## 297 23.907751
## 298 25.669724
## 299 13.412851
## 300 15.346036
## 301 31.304689
## 302 15.845649
## 303 10.570833
## 304 12.247821
## 305 57.439944
## 306 75.822747
## 307 18.180651
## 308 14.561789
## 309 42.497171
## 310 20.243232
## 311 52.249817
## 312 17.897524
## 313 23.399812
## 314 28.965761
## 315 22.270639
## 316 14.329192
## 317 12.203858
## 318 30.953041
## 319 25.246210
## 320 9.067701
## 321 11.489359
## 322 68.448580
## 323 13.485433
## 324 13.268723
## 325 15.651162
## 326 32.151561
## 327 64.521264
## 328 16.253936
## 329 27.193109
## 330 64.779665
## 331 12.334283
## 332 38.737798
## 333 17.643801
## 334 13.292436
## 335 18.135360
## 336 58.801595
## 337 19.497251
## 338 81.565213
## 339 80.018006
## 340 11.647083
## 341 20.794356
## 342 17.177857
## 343 20.967524
## 344 28.535255
## 345 36.351706
## 346 12.478514
## 347 11.039106
## 348 29.388449
## 349 24.310852
## 350 15.912316
## 351 14.852984
## 352 20.319027
## 353 17.279339
## 354 75.930028
## 355 25.243541
## 356 85.061354
## 357 16.743136
## 358 10.418452
## 359 76.239847
## 360 43.812969
## 361 25.636835
## 362 14.307942
## 363 12.012894
## 364 14.923895
## 365 26.189399
## 366 95.558434
## 367 11.669493
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## 369 9.813867
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## 372 71.957903
## 373 11.094146
## 374 12.012992
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## 377 20.743203
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## 379 16.835671
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## 383 15.111341
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## 392 14.307289
## 393 27.097148
## 394 17.877386
## 395 17.697921
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## 402 28.249016
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## 405 21.975933
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## 407 17.445608
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## 409 57.928653
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## 412 21.645105
## 413 19.793397
## 414 16.159871
## 415 24.710725
## 416 11.336343
## 417 11.434840
## 418 69.568230
## 419 14.941806
## 420 16.156974
## 421 19.372908
## 422 15.985596
## 423 35.614147
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## 425 96.213674
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## 430 14.353710
## 431 18.075626
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## 436 69.041333
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## 443 25.663121
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## 445 14.457822
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## 479 12.380137
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## 513 19.589952
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## 10908 20.258416
## 10909 16.029768
## 10910 52.184746
## 10911 49.704494
## 10912 25.448890
## 10913 13.446169
## 10914 12.445639
## 10915 48.902471
## 10916 88.103570
## 10917 13.081679
## 10918 20.294417
## 10919 17.223440
## 10920 16.247446
## 10921 21.503861
## 10922 12.837168
## 10923 9.705052
## 10924 16.035874
## 10925 15.154101
## 10926 18.209125
## 10927 97.778587
## 10928 19.449106
## 10929 58.240402
## 10930 46.078820
## 10931 20.308246
## 10932 21.090766
## 10933 12.007806
## 10934 14.858125
## 10935 8.180373
## 10936 25.810701
## 10937 14.174967
## 10938 20.766059
## 10939 16.907383
## 10940 37.057367
## 10941 35.374905
## 10942 13.768746
## 10943 18.151869
## 10944 13.603109
## 10945 12.938002
## 10946 7.957519
## 10947 41.889056
## 10948 9.168904
## 10949 17.645880
## 10950 54.557998
## 10951 27.656857
## 10952 66.276140
## 10953 30.223798
## 10954 34.508291
## 10955 15.744313
## 10956 22.619743
## 10957 14.449520
## 10958 11.599328
## 10959 9.747227
## 10960 14.178683
## 10961 16.054065
## 10962 23.590841
## 10963 8.975422
## 10964 10.581211
## 10965 14.905378
## 10966 11.974497
## 10967 11.386526
## 10968 14.992470
## 10969 19.302491
## 10970 37.873419
## 10971 19.596346
## 10972 71.377765
## 10973 14.355188
## 10974 20.109391
## 10975 11.125158
## 10976 72.118734
## 10977 19.455395
## 10978 36.711516
## 10979 16.956295
## 10980 47.439666
## 10981 24.656728
## 10982 28.381209
## 10983 13.929672
## 10984 10.795135
## 10985 26.811604
## 10986 14.758259
## 10987 19.440728
## 10988 74.017618
## 10989 15.846433
## 10990 24.819918
## 10991 14.321100
## 10992 44.451304
## 10993 14.835775
## 10994 11.708215
## 10995 16.438981
## 10996 10.280841
## 10997 25.674848
## 10998 28.102643
## 10999 15.603669
## 11000 15.296152
## 11001 33.921380
## 11002 67.855782
## 11003 13.826900
## 11004 15.114079
## 11005 18.093558
## 11006 11.447427
## 11007 16.435507
## 11008 20.018107
## 11009 20.482932
## 11010 77.916381
## 11011 16.445896
## 11012 15.437559
## 11013 19.699286
## 11014 22.723057
## 11015 28.143064
## 11016 57.229891
## 11017 14.188556
## 11018 19.866406
## 11019 10.611137
## 11020 78.983587
## 11021 12.415547
## 11022 23.106215
## 11023 28.186780
## 11024 66.153638
## 11025 9.956608
## 11026 24.005687
## 11027 26.145613
## 11028 33.895848
## 11029 16.857325
## 11030 22.995535
## 11031 8.024077
## 11032 18.142967
## 11033 11.751777
## 11034 12.805429
## 11035 25.614601
## 11036 13.418158
## 11037 9.988505
## 11038 17.570676
## 11039 27.249860
## 11040 30.368420
## 11041 33.987086
## 11042 68.151256
## 11043 70.574052
## 11044 12.787954
## 11045 23.636606
## 11046 27.850314
## 11047 12.020265
## 11048 20.968258
## 11049 74.796887
## 11050 86.523141
## 11051 18.674093
## 11052 24.867929
## 11053 9.644453
## 11054 14.672022
## 11055 17.837167
## 11056 16.029768
## 11057 14.792646
## 11058 12.144786
## 11059 77.711458
## 11060 11.820320
## 11061 12.637537
## 11062 12.914492
## 11063 57.369836
## 11064 16.319843
## 11065 66.720780
## 11066 29.927655
## 11067 10.843968
## 11068 13.266484
## 11069 38.182492
## 11070 15.776090
## 11071 65.291300
## 11072 16.516352
## 11073 45.650423
## 11074 13.179540
## 11075 21.218745
## 11076 9.866025
## 11077 15.893283
## 11078 20.003323
## 11079 44.397596
## 11080 64.792196
## 11081 19.114254
## 11082 17.095606
## 11083 22.436850
## 11084 52.397937
## 11085 20.917757
## 11086 16.616028
## 11087 72.850878
## 11088 14.698925
## 11089 17.439760
## 11090 34.039490
## 11091 20.355194
## 11092 16.394037
## 11093 21.843307
## 11094 37.409514
## 11095 86.555964
## 11096 13.985675
## 11097 19.512584
## 11098 52.719620
## 11099 33.272788
## 11100 13.822849
## 11101 17.473669
## 11102 12.890667
## 11103 73.505716
## 11104 16.393239
## 11105 12.541924
## 11106 16.525376
## 11107 47.121115
## 11108 41.171991
## 11109 14.809492
## 11110 18.416708
## 11111 15.045984
## Excess.kurtosis.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve
## 1 7.449874149 6.515930e+01
## 2 9.414652256 1.027230e+02
## 3 8.508363784 7.403132e+01
## 4 2.958065943 7.197842e+00
## 5 8.405008383 7.629113e+01
## 6 9.546051121 1.121317e+02
## 7 -0.700808817 -1.200653e+00
## 8 9.443282129 9.737458e+01
## 9 7.851205010 7.080194e+01
## 10 5.702532189 3.634249e+01
## 11 6.059849443 3.815479e+01
## 12 11.629119540 2.026866e+02
## 13 9.792420652 1.126258e+02
## 14 5.497543863 3.273983e+01
## 15 11.044507590 1.419361e+02
## 16 9.713802964 1.295237e+02
## 17 -0.540918677 5.988466e-01
## 18 22.810861060 5.596799e+02
## 19 7.740922135 6.502484e+01
## 20 1.735179630 1.358508e+00
## 21 7.517878845 6.918787e+01
## 22 -1.876387679 2.431297e+00
## 23 11.838557040 1.620363e+02
## 24 5.787466528 3.937256e+01
## 25 9.353872066 9.773376e+01
## 26 6.815905214 4.662379e+01
## 27 4.125156960 1.664678e+01
## 28 2.848738680 6.628035e+00
## 29 11.758067810 1.528897e+02
## 30 6.250715403 3.941360e+01
## 31 10.800263930 1.463933e+02
## 32 0.115883054 -8.196377e-01
## 33 7.431087285 5.833907e+01
## 34 8.401668908 7.158764e+01
## 35 1.706101856 1.403352e+00
## 36 10.424573370 1.128880e+02
## 37 7.775510532 6.980282e+01
## 38 8.498741192 8.501392e+01
## 39 13.953464370 2.289704e+02
## 40 9.514008553 9.538102e+01
## 41 0.841739875 -6.377517e-01
## 42 7.119164178 7.248355e+01
## 43 8.357114893 9.926769e+01
## 44 8.490664705 8.151230e+01
## 45 4.747219416 2.229796e+01
## 46 13.669691180 2.505981e+02
## 47 9.203359217 1.248082e+02
## 48 6.774932869 4.537422e+01
## 49 11.893742350 1.639457e+02
## 50 11.807789470 1.703712e+02
## 51 14.097759510 2.243126e+02
## 52 10.530075490 1.171318e+02
## 53 8.947855846 9.430129e+01
## 54 7.998484715 7.362044e+01
## 55 9.655650399 1.232870e+02
## 56 8.088507973 7.591971e+01
## 57 8.908620450 8.442433e+01
## 58 5.904226293 3.551565e+01
## 59 4.530755412 1.951919e+01
## 60 12.638492500 2.169896e+02
## 61 13.067672600 2.043573e+02
## 62 6.142707674 3.965367e+01
## 63 18.472123440 4.184346e+02
## 64 5.823780289 3.807242e+01
## 65 9.658143197 1.001518e+02
## 66 9.061327574 1.062126e+02
## 67 14.042148620 2.235577e+02
## 68 10.695138350 1.451337e+02
## 69 9.389613296 9.175082e+01
## 70 7.026262799 5.852236e+01
## 71 9.613521780 1.341595e+02
## 72 11.055039870 1.506635e+02
## 73 9.341504038 1.129759e+02
## 74 5.938614333 3.491794e+01
## 75 2.934545546 7.168551e+00
## 76 2.674940606 6.396016e+00
## 77 11.142558260 1.618684e+02
## 78 6.312534394 4.561078e+01
## 79 13.278098210 1.832836e+02
## 80 7.809740763 6.680015e+01
## 81 6.239235941 4.417722e+01
## 82 0.858483317 1.157900e-02
## 83 1.718283135 1.680739e+00
## 84 6.467703319 4.406410e+01
## 85 7.736375912 7.235171e+01
## 86 8.590099462 8.722027e+01
## 87 14.970074230 2.940763e+02
## 88 7.560096807 6.096026e+01
## 89 9.595852078 1.179152e+02
## 90 6.234984710 4.015793e+01
## 91 3.501441472 1.035561e+01
## 92 7.620374740 7.581119e+01
## 93 11.324040690 1.449861e+02
## 94 16.611568270 3.598311e+02
## 95 9.654999002 1.053947e+02
## 96 9.987386854 1.017108e+02
## 97 12.347547360 1.773937e+02
## 98 12.880816410 2.338960e+02
## 99 8.436628605 8.722366e+01
## 100 17.303535860 3.638951e+02
## 101 9.588639417 1.100132e+02
## 102 8.525313322 9.651838e+01
## 103 12.563882660 1.808386e+02
## 104 2.913488041 7.745976e+00
## 105 8.806097052 7.995946e+01
## 106 9.324247322 9.352596e+01
## 107 8.795951878 9.556161e+01
## 108 11.963871520 1.767908e+02
## 109 14.871933070 2.335907e+02
## 110 6.403328919 4.419213e+01
## 111 10.144782760 1.118042e+02
## 112 14.405699030 2.916839e+02
## 113 9.407370847 1.117509e+02
## 114 12.349429740 1.825617e+02
## 115 10.556166940 1.156892e+02
## 116 28.137282430 8.801561e+02
## 117 14.950152750 2.462772e+02
## 118 22.573537730 5.322214e+02
## 119 9.321298743 9.914920e+01
## 120 6.387733991 4.334347e+01
## 121 4.364827610 1.846770e+01
## 122 8.557061818 8.685427e+01
## 123 0.078518481 -1.173577e+00
## 124 8.168925188 8.229515e+01
## 125 12.502274000 2.158325e+02
## 126 7.402200439 4.981709e+01
## 127 8.059995570 6.359337e+01
## 128 7.044723014 5.405241e+01
## 129 14.924965440 3.085623e+02
## 130 13.479965270 1.987125e+02
## 131 7.933097984 8.385962e+01
## 132 9.534335567 1.047607e+02
## 133 6.072215931 3.823599e+01
## 134 2.384960386 4.001859e+00
## 135 3.073278401 8.202738e+00
## 136 5.836690022 3.497496e+01
## 137 8.976223330 9.509154e+01
## 138 21.504922620 5.086852e+02
## 139 2.633799311 5.419625e+00
## 140 5.384701738 3.019261e+01
## 141 8.065141303 6.960499e+01
## 142 10.458459590 1.172032e+02
## 143 14.606074240 2.458091e+02
## 144 10.794037680 1.701500e+02
## 145 7.749978450 6.045060e+01
## 146 6.508806491 4.434794e+01
## 147 -0.169285978 -1.903602e+00
## 148 8.356151236 8.124054e+01
## 149 10.603200280 1.248908e+02
## 150 7.831107155 7.593746e+01
## 151 13.372199150 2.183875e+02
## 152 11.418078240 1.706096e+02
## 153 7.861436973 6.438405e+01
## 154 11.428532530 1.801409e+02
## 155 9.983023816 1.136425e+02
## 156 8.869674605 1.055824e+02
## 157 -1.186196012 -2.246940e-01
## 158 10.253072360 1.298854e+02
## 159 1.693892210 1.348607e+00
## 160 0.901047513 5.563160e-01
## 161 6.535209822 4.461362e+01
## 162 11.538678690 1.744031e+02
## 163 8.738222472 1.011514e+02
## 164 9.979061296 1.357854e+02
## 165 11.194429790 1.312518e+02
## 166 7.211629975 5.414400e+01
## 167 4.020460464 1.551974e+01
## 168 3.969981663 1.497790e+01
## 169 7.860980927 6.913862e+01
## 170 12.007966120 1.997906e+02
## 171 8.510258639 8.645863e+01
## 172 9.781064313 9.977507e+01
## 173 0.632559380 -1.547940e+00
## 174 12.105824530 1.832667e+02
## 175 9.228698148 1.092857e+02
## 176 6.499651157 4.306362e+01
## 177 9.902157411 1.302123e+02
## 178 2.181897698 2.803190e+00
## 179 5.091572704 2.691228e+01
## 180 9.779744598 1.059009e+02
## 181 11.400625210 1.565578e+02
## 182 3.444348735 1.116471e+01
## 183 11.058028750 1.271434e+02
## 184 10.266219860 1.160803e+02
## 185 1.516937446 1.262355e+00
## 186 16.587966390 3.714529e+02
## 187 6.539225555 4.798776e+01
## 188 -0.613265858 -1.512690e+00
## 189 7.361531784 6.771918e+01
## 190 0.253562477 -9.361037e-01
## 191 17.110807760 3.585045e+02
## 192 6.923330065 4.870415e+01
## 193 -0.363839554 -1.348619e+00
## 194 5.887304090 3.775569e+01
## 195 10.644499980 1.342461e+02
## 196 10.532035580 1.472591e+02
## 197 -1.169558054 -1.309995e-01
## 198 13.587201520 2.444614e+02
## 199 10.403072370 1.186298e+02
## 200 -0.820295935 -1.159351e+00
## 201 8.684067361 8.457740e+01
## 202 3.928965722 1.482668e+01
## 203 7.493529924 6.533365e+01
## 204 13.489588780 2.274718e+02
## 205 1.233599293 -1.664359e-01
## 206 -0.687955054 -9.232003e-01
## 207 10.007405200 1.157067e+02
## 208 13.173461160 1.786201e+02
## 209 12.959685360 2.390897e+02
## 210 11.609225630 1.710234e+02
## 211 9.869735634 1.182008e+02
## 212 7.091702161 5.407948e+01
## 213 8.615121645 9.289865e+01
## 214 12.185737600 1.782883e+02
## 215 13.806275650 2.304950e+02
## 216 9.166237346 9.682954e+01
## 217 9.235035880 9.024698e+01
## 218 4.732799763 2.308970e+01
## 219 12.128238450 1.553228e+02
## 220 13.401365860 2.174031e+02
## 221 9.171303117 1.035690e+02
## 222 10.165905410 1.069360e+02
## 223 9.793335910 1.257820e+02
## 224 9.214074319 9.848470e+01
## 225 11.930533230 2.016532e+02
## 226 0.687108544 -1.770760e-01
## 227 7.012918477 5.059069e+01
## 228 3.791869842 1.413001e+01
## 229 11.852878360 1.500963e+02
## 230 11.779449100 1.955969e+02
## 231 17.949341630 3.695529e+02
## 232 6.829105911 4.972000e+01
## 233 6.854449719 4.936971e+01
## 234 10.986932840 1.432170e+02
## 235 8.497038558 9.402279e+01
## 236 7.086230907 5.422526e+01
## 237 5.589647701 3.260252e+01
## 238 11.007447510 1.592803e+02
## 239 5.034427743 2.543866e+01
## 240 6.291798542 4.225289e+01
## 241 11.505463800 1.814023e+02
## 242 7.034078746 5.686715e+01
## 243 5.817459209 3.432272e+01
## 244 10.930194370 1.280259e+02
## 245 11.388416310 1.437557e+02
## 246 9.049086227 9.101578e+01
## 247 0.018839747 -1.330801e+00
## 248 10.593090390 1.454333e+02
## 249 7.107382559 5.364192e+01
## 250 10.328263880 1.302294e+02
## 251 11.338296650 1.439285e+02
## 252 11.947498450 1.569475e+02
## 253 0.909743039 -8.210958e-03
## 254 9.004847741 8.518559e+01
## 255 10.128382110 1.217323e+02
## 256 0.195576924 -7.574973e-01
## 257 6.971780245 5.555050e+01
## 258 11.283525020 1.620751e+02
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## 261 8.655813110 7.630278e+01
## 262 10.680071200 1.541650e+02
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## 264 14.637708310 2.900194e+02
## 265 10.488842360 1.224311e+02
## 266 8.521578097 8.879063e+01
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## 269 -0.450082466 -1.443497e+00
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## 1895 2.185654940 2.876931e+00
## 1896 6.123176817 3.875713e+01
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## 1898 0.613603196 1.487227e+01
## 1899 0.989217856 -5.336034e-01
## 1900 12.123400390 1.643967e+02
## 1901 1.220369342 -1.357132e-01
## 1902 5.496571075 3.130575e+01
## 1903 15.824082990 3.250530e+02
## 1904 2.061246836 3.273828e+00
## 1905 3.324040394 1.019779e+01
## 1906 16.836927110 3.658467e+02
## 1907 9.243711120 9.225708e+01
## 1908 9.582518113 1.180876e+02
## 1909 13.791514960 1.969727e+02
## 1910 9.736878098 1.031816e+02
## 1911 11.961949690 1.640767e+02
## 1912 2.468507760 5.532072e+00
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## 1914 6.858409769 5.580627e+01
## 1915 7.842075704 7.316356e+01
## 1916 8.322723721 7.820528e+01
## 1917 5.231418057 2.851797e+01
## 1918 11.535062750 1.852705e+02
## 1919 3.960305292 1.456338e+01
## 1920 15.885897430 2.800440e+02
## 1921 8.786325916 1.036945e+02
## 1922 8.439767942 7.771267e+01
## 1923 11.130535070 1.279593e+02
## 1924 9.613174922 1.045099e+02
## 1925 7.274041194 7.077721e+01
## 1926 10.873196560 1.393046e+02
## 1927 13.619709490 2.125109e+02
## 1928 10.169388140 1.376383e+02
## 1929 9.396262298 1.030994e+02
## 1930 8.735114846 9.827912e+01
## 1931 2.135822594 4.428894e+00
## 1932 3.611961561 1.210399e+01
## 1933 6.972411015 5.371118e+01
## 1934 1.140440759 6.275402e-01
## 1935 2.912674529 7.865558e+00
## 1936 16.359058250 3.194215e+02
## 1937 2.207758783 3.461660e+00
## 1938 9.168151163 1.040836e+02
## 1939 4.372821120 1.820433e+01
## 1940 9.928026567 1.295081e+02
## 1941 10.544885450 1.222615e+02
## 1942 9.224926925 8.967929e+01
## 1943 7.742797869 9.212259e+01
## 1944 9.013115751 9.718986e+01
## 1945 7.491698486 6.539476e+01
## 1946 10.629088540 1.299148e+02
## 1947 10.606203400 1.395969e+02
## 1948 2.042045117 2.616415e+00
## 1949 7.258005568 5.463889e+01
## 1950 8.529005068 7.189935e+01
## 1951 7.121887919 5.393786e+01
## 1952 9.981235918 1.537030e+02
## 1953 9.283710519 9.613555e+01
## 1954 12.930152560 2.040135e+02
## 1955 9.808892861 1.327595e+02
## 1956 12.176596100 1.843163e+02
## 1957 0.205233320 -1.725200e+00
## 1958 7.211573168 6.259113e+01
## 1959 14.428005890 2.429658e+02
## 1960 2.405720339 4.162780e+00
## 1961 27.446112730 8.306386e+02
## 1962 3.583205806 1.165153e+01
## 1963 1.226451381 2.259751e+00
## 1964 -0.361278215 -1.723632e+00
## 1965 1.982008150 2.898798e+00
## 1966 10.087307280 1.424880e+02
## 1967 7.954436097 7.196016e+01
## 1968 4.295452708 1.741990e+01
## 1969 4.472868643 2.046711e+01
## 1970 12.677630320 2.140032e+02
## 1971 9.886036755 9.912494e+01
## 1972 9.866782711 1.266392e+02
## 1973 11.536390070 1.891087e+02
## 1974 7.961985107 6.620942e+01
## 1975 17.553075590 3.349614e+02
## 1976 10.383348310 1.228297e+02
## 1977 6.748053193 5.074377e+01
## 1978 8.504414895 8.714250e+01
## 1979 9.638634521 1.244695e+02
## 1980 12.143557220 1.762531e+02
## 1981 0.920672860 -3.027592e-01
## 1982 7.197343664 6.078299e+01
## 1983 10.195937140 1.390999e+02
## 1984 10.768784670 1.298348e+02
## 1985 7.812303703 7.118191e+01
## 1986 4.728046059 2.237161e+01
## 1987 0.553429570 -4.128701e-01
## 1988 18.704532220 3.746458e+02
## 1989 0.722125050 -1.201452e+00
## 1990 9.586297723 9.457024e+01
## 1991 6.859046095 4.894788e+01
## 1992 7.359988103 5.844123e+01
## 1993 6.871041387 5.141233e+01
## 1994 7.730307834 6.032719e+01
## 1995 9.895163986 1.289361e+02
## 1996 9.579694373 1.013906e+02
## 1997 11.016072520 1.437991e+02
## 1998 1.790541316 1.328747e+00
## 1999 12.095032300 1.737445e+02
## 2000 4.805558490 2.479350e+01
## 2001 10.393759090 1.171369e+02
## 2002 8.909781575 1.043526e+02
## 2003 9.583179377 9.921797e+01
## 2004 1.509937251 1.800585e+00
## 2005 11.174622750 1.367864e+02
## 2006 8.186273206 8.329271e+01
## 2007 6.177782765 3.780271e+01
## 2008 9.281183087 1.077346e+02
## 2009 8.392178492 8.117005e+01
## 2010 10.198269510 1.063424e+02
## 2011 -0.399639203 -1.034550e+00
## 2012 5.832529474 3.779577e+01
## 2013 7.786616824 6.882740e+01
## 2014 -0.324682974 -1.790750e+00
## 2015 7.779946987 7.506184e+01
## 2016 5.865494906 3.521488e+01
## 2017 4.500678569 1.929413e+01
## 2018 14.107285270 2.310515e+02
## 2019 9.375479552 9.782010e+01
## 2020 8.189990981 7.321134e+01
## 2021 11.939384930 1.769342e+02
## 2022 17.382170150 3.240659e+02
## 2023 12.062264090 1.616477e+02
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## 2028 13.684515760 2.648545e+02
## 2029 11.372804660 1.491300e+02
## 2030 1.732743563 1.567143e+00
## 2031 -1.039828229 5.233383e-01
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## [ reached 'max' / getOption("max.print") -- omitted 1417 rows ] # Generating a summary of the dataset with histograms
skimmed <- skim(df_train)
skimmed| Name | df_train |
| Number of rows | 12528 |
| Number of columns | 9 |
| _______________________ | |
| Column type frequency: | |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Mean.of.the.integrated.profile | 0 | 1 | 111.04 | 25.67 | 5.81 | 100.87 | 115.18 | 127.11 | 189.73 | ▁▁▇▇▁ |
| Standard.deviation.of.the.integrated.profile | 0 | 1 | 46.52 | 6.80 | 24.77 | 42.36 | 46.93 | 50.98 | 91.81 | ▂▇▂▁▁ |
| Excess.kurtosis.of.the.integrated.profile | 0 | 1 | 0.48 | 1.07 | -1.74 | 0.02 | 0.22 | 0.47 | 8.07 | ▇▇▁▁▁ |
| Skewness.of.the.integrated.profile | 0 | 1 | 1.78 | 6.21 | -1.79 | -0.19 | 0.20 | 0.93 | 68.10 | ▇▁▁▁▁ |
| Mean.of.the.DM.SNR.curve | 0 | 1 | 12.67 | 29.61 | 0.21 | 1.91 | 2.79 | 5.41 | 222.42 | ▇▁▁▁▁ |
| Standard.deviation.of.the.DM.SNR.curve | 0 | 1 | 26.29 | 19.52 | 7.37 | 14.38 | 18.39 | 28.34 | 110.64 | ▇▁▁▁▁ |
| Excess.kurtosis.of.the.DM.SNR.curve | 0 | 1 | 8.33 | 4.54 | -3.14 | 5.80 | 8.45 | 10.73 | 34.54 | ▂▇▂▁▁ |
| Skewness.of.the.DM.SNR.curve | 0 | 1 | 105.84 | 107.54 | -1.98 | 35.49 | 83.25 | 140.37 | 1191.00 | ▇▁▁▁▁ |
| target_class | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
X = df_train[,1:8]
# With the missing values handled, our training dataset is now ready to undergo variable transformations if required.
# caret library provides the following data transformations:
# range: Normalize values so it ranges between 0 and 1
# center: Subtract Mean
# scale: Divide by standard deviation
# BoxCox: Remove skewness leading to normality. Values must be > 0
# YeoJohnson: Like BoxCox, but works for negative values.
# expoTrans: Exponential transformation, works for negative values.
# pca: Replace with principal components
# ica: Replace with independent components
# spatialSign: Project the data to a unit circle
# For our problem, let’s convert all the numeric variables to range between 0 and 1, by setting method=range in preProcess().
# TRANSFORMING ATTRIBUTES
preProcess_range_model <- preProcess(df_train, method='range')
df_train <- predict(preProcess_range_model, newdata = df_train)
apply(df_train[, 1:8], 2, FUN=function(x){c('min'=min(x), 'max'=max(x))})## Mean.of.the.integrated.profile Standard.deviation.of.the.integrated.profile
## min 0 0
## max 1 1
## Excess.kurtosis.of.the.integrated.profile
## min 0
## max 1
## Skewness.of.the.integrated.profile Mean.of.the.DM.SNR.curve
## min 0 0
## max 1 1
## Standard.deviation.of.the.DM.SNR.curve Excess.kurtosis.of.the.DM.SNR.curve
## min 0 0
## max 1 1
## Skewness.of.the.DM.SNR.curve
## min 0
## max 1# APPENDING THE TARGET CLASS
df_train$target_class <- y
head(df_train)## Mean.of.the.integrated.profile Standard.deviation.of.the.integrated.profile
## 1 0.6271345 0.3520604
## 2 0.3868830 0.1701088
## 3 0.6784046 0.4245069
## 4 0.8187495 0.3594142
## 5 0.4294877 0.1692452
## 6 0.6263274 0.3342190
## Excess.kurtosis.of.the.integrated.profile Skewness.of.the.integrated.profile
## 1 0.2154980 0.02544901
## 2 0.2499014 0.07412140
## 3 0.1908153 0.02138459
## 4 0.1551900 0.02318659
## 5 0.2613329 0.07248186
## 6 0.2006342 0.02694417
## Mean.of.the.DM.SNR.curve Standard.deviation.of.the.DM.SNR.curve
## 1 0.013301425 0.10679524
## 2 0.009839668 0.09877398
## 3 0.011386170 0.14517152
## 4 0.077667528 0.37934885
## 5 0.011596886 0.12827877
## 6 0.008206622 0.10585924
## Excess.kurtosis.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve target_class
## 1 0.2810348 0.056276213 0
## 2 0.3331799 0.087763535 0
## 3 0.3091271 0.063713088 0
## 4 0.1618227 0.007690686 0
## 5 0.3063840 0.065607342 0
## 6 0.3366672 0.095650309 0# ANALYSIS OF THE DATASET AFTER DATA TRANSFORMATION
#1) Dimension of the dataset and column names
dim(df_train)## [1] 12528 9colnames(df_train)## [1] "Mean.of.the.integrated.profile"
## [2] "Standard.deviation.of.the.integrated.profile"
## [3] "Excess.kurtosis.of.the.integrated.profile"
## [4] "Skewness.of.the.integrated.profile"
## [5] "Mean.of.the.DM.SNR.curve"
## [6] "Standard.deviation.of.the.DM.SNR.curve"
## [7] "Excess.kurtosis.of.the.DM.SNR.curve"
## [8] "Skewness.of.the.DM.SNR.curve"
## [9] "target_class"sapply(df_train, class)## Mean.of.the.integrated.profile
## "numeric"
## Standard.deviation.of.the.integrated.profile
## "numeric"
## Excess.kurtosis.of.the.integrated.profile
## "numeric"
## Skewness.of.the.integrated.profile
## "numeric"
## Mean.of.the.DM.SNR.curve
## "numeric"
## Standard.deviation.of.the.DM.SNR.curve
## "numeric"
## Excess.kurtosis.of.the.DM.SNR.curve
## "numeric"
## Skewness.of.the.DM.SNR.curve
## "numeric"
## target_class
## "numeric"head(df_train)## Mean.of.the.integrated.profile Standard.deviation.of.the.integrated.profile
## 1 0.6271345 0.3520604
## 2 0.3868830 0.1701088
## 3 0.6784046 0.4245069
## 4 0.8187495 0.3594142
## 5 0.4294877 0.1692452
## 6 0.6263274 0.3342190
## Excess.kurtosis.of.the.integrated.profile Skewness.of.the.integrated.profile
## 1 0.2154980 0.02544901
## 2 0.2499014 0.07412140
## 3 0.1908153 0.02138459
## 4 0.1551900 0.02318659
## 5 0.2613329 0.07248186
## 6 0.2006342 0.02694417
## Mean.of.the.DM.SNR.curve Standard.deviation.of.the.DM.SNR.curve
## 1 0.013301425 0.10679524
## 2 0.009839668 0.09877398
## 3 0.011386170 0.14517152
## 4 0.077667528 0.37934885
## 5 0.011596886 0.12827877
## 6 0.008206622 0.10585924
## Excess.kurtosis.of.the.DM.SNR.curve Skewness.of.the.DM.SNR.curve target_class
## 1 0.2810348 0.056276213 0
## 2 0.3331799 0.087763535 0
## 3 0.3091271 0.063713088 0
## 4 0.1618227 0.007690686 0
## 5 0.3063840 0.065607342 0
## 6 0.3366672 0.095650309 0#2) Summarize the class distribution
percentage <- prop.table(table(df_train$target_class)) * 100
cbind(freq=table(df_train$target_class), percentage=percentage)## freq percentage
## 0 11375 90.796616
## 1 1153 9.203384#3) Correlation between different attributes using Heatmap
corrplot::corrplot(cor(df_train))
#4) Finding the important variables:
# Now that the preprocessing is complete, let’s visually examine how the predictors influence the Y (Purchase).
# In this problem, the X variables are numeric whereas the Y is categorical. So how to gauge if a given X is an
# important predictor of Y? A simple common sense approach is, if you group the X variable by the categories of
# Y, a significant mean shift amongst the X’s groups is a strong indicator (if not the only indicator) that X
# will have a significant role to help predict Y.
# It is possible to watch this shift visually using box plots and density plots.
# In fact, caret’s featurePlot() function makes it so convenient by simply setting the X and Y parameters and plot='box'.
df_train$target_class <- as.factor(df_train$target_class)
featurePlot(x = df_train[, 1:8],
y = df_train$target_class,
plot = "box",
strip=strip.custom(par.strip.text=list(cex=.7)),
scales = list(x = list(relation="free"),
y = list(relation="free")))
featurePlot(x = df_train[, 1:8],
y = df_train$target_class,
plot = "density",
strip=strip.custom(par.strip.text=list(cex=.7)),
scales = list(x = list(relation="free"),
y = list(relation="free")))
#5) Looking at the available ML models algorithms in caret
modelnames <- paste(names(getModelInfo()), collapse=', ')
modelnames## [1] "ada, AdaBag, AdaBoost.M1, adaboost, amdai, ANFIS, avNNet, awnb, awtan, bag, bagEarth, bagEarthGCV, bagFDA, bagFDAGCV, bam, bartMachine, bayesglm, binda, blackboost, blasso, blassoAveraged, bridge, brnn, BstLm, bstSm, bstTree, C5.0, C5.0Cost, C5.0Rules, C5.0Tree, cforest, chaid, CSimca, ctree, ctree2, cubist, dda, deepboost, DENFIS, dnn, dwdLinear, dwdPoly, dwdRadial, earth, elm, enet, evtree, extraTrees, fda, FH.GBML, FIR.DM, foba, FRBCS.CHI, FRBCS.W, FS.HGD, gam, gamboost, gamLoess, gamSpline, gaussprLinear, gaussprPoly, gaussprRadial, gbm_h2o, gbm, gcvEarth, GFS.FR.MOGUL, GFS.LT.RS, GFS.THRIFT, glm.nb, glm, glmboost, glmnet_h2o, glmnet, glmStepAIC, gpls, hda, hdda, hdrda, HYFIS, icr, J48, JRip, kernelpls, kknn, knn, krlsPoly, krlsRadial, lars, lars2, lasso, lda, lda2, leapBackward, leapForward, leapSeq, Linda, lm, lmStepAIC, LMT, loclda, logicBag, LogitBoost, logreg, lssvmLinear, lssvmPoly, lssvmRadial, lvq, M5, M5Rules, manb, mda, Mlda, mlp, mlpKerasDecay, mlpKerasDecayCost, mlpKerasDropout, mlpKerasDropoutCost, mlpML, mlpSGD, mlpWeightDecay, mlpWeightDecayML, monmlp, msaenet, multinom, mxnet, mxnetAdam, naive_bayes, nb, nbDiscrete, nbSearch, neuralnet, nnet, nnls, nodeHarvest, null, OneR, ordinalNet, ordinalRF, ORFlog, ORFpls, ORFridge, ORFsvm, ownn, pam, parRF, PART, partDSA, pcaNNet, pcr, pda, pda2, penalized, PenalizedLDA, plr, pls, plsRglm, polr, ppr, pre, PRIM, protoclass, qda, QdaCov, qrf, qrnn, randomGLM, ranger, rbf, rbfDDA, Rborist, rda, regLogistic, relaxo, rf, rFerns, RFlda, rfRules, ridge, rlda, rlm, rmda, rocc, rotationForest, rotationForestCp, rpart, rpart1SE, rpart2, rpartCost, rpartScore, rqlasso, rqnc, RRF, RRFglobal, rrlda, RSimca, rvmLinear, rvmPoly, rvmRadial, SBC, sda, sdwd, simpls, SLAVE, slda, smda, snn, sparseLDA, spikeslab, spls, stepLDA, stepQDA, superpc, svmBoundrangeString, svmExpoString, svmLinear, svmLinear2, svmLinear3, svmLinearWeights, svmLinearWeights2, svmPoly, svmRadial, svmRadialCost, svmRadialSigma, svmRadialWeights, svmSpectrumString, tan, tanSearch, treebag, vbmpRadial, vglmAdjCat, vglmContRatio, vglmCumulative, widekernelpls, WM, wsrf, xgbDART, xgbLinear, xgbTree, xyf"# Selecting three models to implement: MARS, KNN from caret and Neural Networks
#6) SPLITTING DATASET INTRO TRAIN AND TEST DATA
set.seed(100)
indexes <- createDataPartition(as.factor(df_train$target_class), p = 0.8, list = F)
train <- df_train[indexes, ]
test <- df_train[-indexes, ]
#8) BUILDING MODELS
# I. Train a Multivariate Adaptive Regression Splines (MARS) model
model_mars = train(target_class ~ ., data=train, method='earth')## Loading required package: earth
## Loading required package: Formula
## Loading required package: plotmo
## Loading required package: plotrix
## Loading required package: TeachingDemos## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred fitted <- predict(model_mars)
model_mars## Multivariate Adaptive Regression Spline
##
## 10023 samples
## 8 predictor
## 2 classes: '0', '1'
##
## No pre-processing
## Resampling: Bootstrapped (25 reps)
## Summary of sample sizes: 10023, 10023, 10023, 10023, 10023, 10023, ...
## Resampling results across tuning parameters:
##
## nprune Accuracy Kappa
## 2 0.9771226 0.8515529
## 8 0.9795605 0.8710113
## 14 0.9800604 0.8747141
##
## Tuning parameter 'degree' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were nprune = 14 and degree = 1. plot(model_mars, main="Model Accuracies with MARS")
varimp_mars <- varImp(model_mars)
plot(varimp_mars, main="Variable Importance with MARS")
# Predict on test Data
predicted_mars <- predict(model_mars, test)
head(predicted_mars)## [1] 0 0 0 1 0 0
## Levels: 0 1 # Summarize results
confusionMatrix(predicted_mars, test[,9])## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 2263 43
## 1 12 187
##
## Accuracy : 0.978
## 95% CI : (0.9715, 0.9834)
## No Information Rate : 0.9082
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8599
##
## Mcnemar's Test P-Value : 5.228e-05
##
## Sensitivity : 0.9947
## Specificity : 0.8130
## Pos Pred Value : 0.9814
## Neg Pred Value : 0.9397
## Prevalence : 0.9082
## Detection Rate : 0.9034
## Detection Prevalence : 0.9206
## Balanced Accuracy : 0.9039
##
## 'Positive' Class : 0
## # II. KNN (K-NEAREST NEIGHBOURS)
trctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 3)
set.seed(100)
knn_fit <- train(target_class ~., data = train, method = "knn",
trControl=trctrl,
preProcess = c("center", "scale"),
tuneLength = 10)
knn_fit## k-Nearest Neighbors
##
## 10023 samples
## 8 predictor
## 2 classes: '0', '1'
##
## Pre-processing: centered (8), scaled (8)
## Resampling: Cross-Validated (10 fold, repeated 3 times)
## Summary of sample sizes: 9021, 9021, 9021, 9021, 9020, 9021, ...
## Resampling results across tuning parameters:
##
## k Accuracy Kappa
## 5 0.9769867 0.8553168
## 7 0.9775185 0.8579382
## 9 0.9775519 0.8578963
## 11 0.9778512 0.8595764
## 13 0.9776516 0.8579855
## 15 0.9778512 0.8588325
## 17 0.9773522 0.8554451
## 19 0.9772191 0.8543584
## 21 0.9768202 0.8512181
## 23 0.9765541 0.8493428
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was k = 15. # Predict on test Data
predicted_knn <- predict(knn_fit, test)
head(predicted_knn)## [1] 0 0 0 1 0 0
## Levels: 0 1 plot(knn_fit, main="Model Accuracies with KNN")
varimp_KNN <- varImp(knn_fit)
plot(varimp_KNN, main="Variable Importance with KNN")
# Summarize results
confusionMatrix(predicted_knn, test[,9])## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 2266 48
## 1 9 182
##
## Accuracy : 0.9772
## 95% CI : (0.9706, 0.9827)
## No Information Rate : 0.9082
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8523
##
## Mcnemar's Test P-Value : 4.823e-07
##
## Sensitivity : 0.9960
## Specificity : 0.7913
## Pos Pred Value : 0.9793
## Neg Pred Value : 0.9529
## Prevalence : 0.9082
## Detection Rate : 0.9046
## Detection Prevalence : 0.9238
## Balanced Accuracy : 0.8937
##
## 'Positive' Class : 0
## # III. Neural Network
train_params <- trainControl(method = "repeatedcv", number = 10, repeats=3)
nnet_model <- train(train[,-9], as.factor(train$target_class),
method = "nnet",trControl= train_params,
preProcess=c("scale","center"),
na.action = na.omit)## # weights: 11
## initial value 7213.800538
## iter 10 value 1441.647191
## iter 20 value 1143.935442
## iter 30 value 908.576618
## iter 40 value 890.049634
## iter 50 value 884.403631
## iter 60 value 882.762688
## iter 70 value 881.249615
## iter 80 value 881.242187
## iter 90 value 881.138421
## iter 100 value 880.749896
## final value 880.749896
## stopped after 100 iterations
## # weights: 31
## initial value 4909.107192
## iter 10 value 723.101302
## iter 20 value 659.207167
## iter 30 value 637.119803
## iter 40 value 611.705725
## iter 50 value 603.326151
## iter 60 value 600.925768
## iter 70 value 599.121980
## iter 80 value 598.172756
## iter 90 value 597.650244
## iter 100 value 596.952033
## final value 596.952033
## stopped after 100 iterations
## # weights: 51
## initial value 2972.937807
## iter 10 value 682.669182
## iter 20 value 605.573772
## iter 30 value 588.489056
## iter 40 value 572.713360
## iter 50 value 564.523809
## iter 60 value 561.610756
## iter 70 value 560.775313
## iter 80 value 559.129441
## iter 90 value 557.629907
## iter 100 value 556.981196
## final value 556.981196
## stopped after 100 iterations
## # weights: 11
## initial value 5173.636016
## iter 10 value 1305.224000
## iter 20 value 902.528503
## iter 30 value 799.394774
## iter 40 value 690.824056
## iter 50 value 682.967453
## iter 60 value 682.629804
## final value 682.629796
## converged
## # weights: 31
## initial value 6474.180213
## iter 10 value 676.169427
## iter 20 value 649.201578
## iter 30 value 643.670432
## iter 40 value 633.413573
## iter 50 value 630.006482
## iter 60 value 627.868053
## iter 70 value 627.241230
## iter 80 value 626.121969
## iter 90 value 625.394656
## iter 100 value 624.475392
## final value 624.475392
## stopped after 100 iterations
## # weights: 51
## initial value 9715.785578
## iter 10 value 782.163739
## iter 20 value 669.218661
## iter 30 value 640.884745
## iter 40 value 627.918796
## iter 50 value 620.044324
## iter 60 value 612.398274
## iter 70 value 609.606929
## iter 80 value 604.359392
## iter 90 value 601.415490
## iter 100 value 600.698170
## final value 600.698170
## stopped after 100 iterations
## # weights: 11
## initial value 8093.581641
## iter 10 value 2982.774406
## iter 20 value 1478.458303
## iter 30 value 1411.180458
## iter 40 value 1118.348978
## iter 50 value 781.843413
## iter 60 value 666.204760
## iter 70 value 664.643787
## iter 80 value 662.889418
## iter 90 value 662.633364
## iter 100 value 662.545477
## final value 662.545477
## stopped after 100 iterations
## # weights: 31
## initial value 8008.333442
## iter 10 value 765.690235
## iter 20 value 653.693992
## iter 30 value 640.925992
## iter 40 value 633.623484
## iter 50 value 630.083148
## iter 60 value 626.713675
## iter 70 value 623.592397
## iter 80 value 623.031536
## iter 90 value 622.657367
## iter 100 value 622.164418
## final value 622.164418
## stopped after 100 iterations
## # weights: 51
## initial value 5166.178529
## iter 10 value 723.236716
## iter 20 value 629.502458
## iter 30 value 605.884367
## iter 40 value 595.637572
## iter 50 value 585.631408
## iter 60 value 582.637674
## iter 70 value 578.577291
## iter 80 value 575.506708
## iter 90 value 574.171383
## iter 100 value 573.526065
## final value 573.526065
## stopped after 100 iterations
## # weights: 11
## initial value 4527.522562
## iter 10 value 1320.842450
## iter 20 value 762.849385
## iter 30 value 677.250229
## iter 40 value 665.348299
## iter 50 value 660.549891
## iter 60 value 660.223474
## iter 70 value 659.881959
## iter 80 value 659.853720
## iter 80 value 659.853715
## final value 659.853674
## converged
## # weights: 31
## initial value 4557.309586
## iter 10 value 882.003101
## iter 20 value 721.277754
## iter 30 value 655.074474
## iter 40 value 631.319198
## iter 50 value 620.763133
## iter 60 value 615.449490
## iter 70 value 609.164084
## iter 80 value 607.734197
## iter 90 value 606.137123
## iter 100 value 606.005091
## final value 606.005091
## stopped after 100 iterations
## # weights: 51
## initial value 4858.132843
## iter 10 value 755.792983
## iter 20 value 639.132751
## iter 30 value 612.372289
## iter 40 value 602.113230
## iter 50 value 591.243756
## iter 60 value 581.441403
## iter 70 value 578.076735
## iter 80 value 574.770669
## iter 90 value 571.988753
## iter 100 value 571.471583
## final value 571.471583
## stopped after 100 iterations
## # weights: 11
## initial value 7591.600993
## iter 10 value 2362.609900
## iter 20 value 2358.568361
## final value 2358.557949
## converged
## # weights: 31
## initial value 7959.654983
## iter 10 value 821.114046
## iter 20 value 701.359563
## iter 30 value 659.909652
## iter 40 value 637.033126
## iter 50 value 626.009743
## iter 60 value 622.389673
## iter 70 value 621.160104
## iter 80 value 619.677874
## iter 90 value 616.838072
## iter 100 value 616.549331
## final value 616.549331
## stopped after 100 iterations
## # weights: 51
## initial value 5961.776076
## iter 10 value 701.158530
## iter 20 value 645.667534
## iter 30 value 625.047097
## iter 40 value 616.352440
## iter 50 value 611.519245
## iter 60 value 606.908897
## iter 70 value 604.026032
## iter 80 value 602.331648
## iter 90 value 601.400033
## iter 100 value 600.594141
## final value 600.594141
## stopped after 100 iterations
## # weights: 11
## initial value 4785.221727
## iter 10 value 1281.783556
## iter 20 value 738.246994
## iter 30 value 666.460436
## iter 40 value 660.621719
## iter 50 value 660.032731
## iter 60 value 659.975957
## iter 70 value 659.912094
## iter 70 value 659.912094
## final value 659.912094
## converged
## # weights: 31
## initial value 8793.063552
## iter 10 value 845.277198
## iter 20 value 660.627465
## iter 30 value 639.879223
## iter 40 value 627.185168
## iter 50 value 618.654352
## iter 60 value 614.459800
## iter 70 value 612.583613
## iter 80 value 610.488939
## iter 90 value 609.784493
## iter 100 value 608.971088
## final value 608.971088
## stopped after 100 iterations
## # weights: 51
## initial value 4963.650679
## iter 10 value 942.005382
## iter 20 value 687.969907
## iter 30 value 634.179062
## iter 40 value 617.137124
## iter 50 value 602.295782
## iter 60 value 591.794349
## iter 70 value 581.296195
## iter 80 value 576.133789
## iter 90 value 572.760432
## iter 100 value 570.857035
## final value 570.857035
## stopped after 100 iterations
## # weights: 11
## initial value 7319.069705
## iter 10 value 888.266281
## iter 20 value 712.921317
## iter 30 value 682.241592
## iter 40 value 669.493737
## iter 50 value 667.241501
## iter 60 value 666.643675
## iter 70 value 666.250776
## iter 80 value 666.243326
## iter 90 value 666.147550
## iter 100 value 666.136177
## final value 666.136177
## stopped after 100 iterations
## # weights: 31
## initial value 5548.647979
## iter 10 value 738.044111
## iter 20 value 650.801551
## iter 30 value 631.514137
## iter 40 value 615.970830
## iter 50 value 602.466176
## iter 60 value 597.824033
## iter 70 value 597.255120
## iter 80 value 596.202743
## iter 90 value 595.872660
## iter 100 value 595.358811
## final value 595.358811
## stopped after 100 iterations
## # weights: 51
## initial value 4968.928100
## iter 10 value 965.495097
## iter 20 value 711.530375
## iter 30 value 628.969566
## iter 40 value 597.558409
## iter 50 value 583.219152
## iter 60 value 578.146527
## iter 70 value 576.160208
## iter 80 value 574.465215
## iter 90 value 572.966528
## iter 100 value 571.145215
## final value 571.145215
## stopped after 100 iterations
## # weights: 11
## initial value 9585.426720
## iter 10 value 2711.918275
## final value 2710.921967
## converged
## # weights: 31
## initial value 5699.180351
## iter 10 value 813.229619
## iter 20 value 706.275837
## iter 30 value 643.185600
## iter 40 value 631.181164
## iter 50 value 628.577989
## iter 60 value 627.357390
## iter 70 value 627.094332
## iter 80 value 626.967756
## iter 90 value 626.948538
## final value 626.948524
## converged
## # weights: 51
## initial value 5026.910290
## iter 10 value 657.034393
## iter 20 value 629.818773
## iter 30 value 617.989314
## iter 40 value 612.034922
## iter 50 value 607.851673
## iter 60 value 603.490947
## iter 70 value 600.842319
## iter 80 value 598.444647
## iter 90 value 596.200555
## iter 100 value 593.463777
## final value 593.463777
## stopped after 100 iterations
## # weights: 11
## initial value 4317.576834
## iter 10 value 1014.436507
## iter 20 value 886.630462
## iter 30 value 848.981728
## iter 40 value 834.107431
## iter 50 value 793.363511
## iter 60 value 699.554434
## iter 70 value 678.065207
## iter 80 value 669.156941
## iter 90 value 667.209304
## iter 100 value 666.656618
## final value 666.656618
## stopped after 100 iterations
## # weights: 31
## initial value 5291.282370
## iter 10 value 773.968855
## iter 20 value 661.592661
## iter 30 value 641.918406
## iter 40 value 623.120801
## iter 50 value 615.895588
## iter 60 value 610.623404
## iter 70 value 607.209350
## iter 80 value 606.262929
## iter 90 value 605.388005
## iter 100 value 603.190282
## final value 603.190282
## stopped after 100 iterations
## # weights: 51
## initial value 3769.663754
## iter 10 value 689.600632
## iter 20 value 611.350266
## iter 30 value 590.758846
## iter 40 value 573.427694
## iter 50 value 564.807276
## iter 60 value 559.747372
## iter 70 value 557.206546
## iter 80 value 555.655969
## iter 90 value 555.407057
## iter 100 value 554.424579
## final value 554.424579
## stopped after 100 iterations
## # weights: 11
## initial value 6373.599030
## iter 10 value 1353.891366
## iter 20 value 665.851853
## iter 30 value 629.691482
## iter 40 value 628.108053
## iter 50 value 627.047120
## iter 60 value 626.901675
## iter 70 value 626.877535
## iter 80 value 626.839904
## final value 626.839808
## converged
## # weights: 31
## initial value 6038.332793
## iter 10 value 686.857447
## iter 20 value 614.401691
## iter 30 value 594.062952
## iter 40 value 588.192590
## iter 50 value 587.602678
## iter 60 value 587.188446
## iter 70 value 587.086183
## iter 80 value 587.061597
## iter 90 value 586.459063
## iter 100 value 578.598408
## final value 578.598408
## stopped after 100 iterations
## # weights: 51
## initial value 10874.964355
## iter 10 value 648.337267
## iter 20 value 575.948824
## iter 30 value 554.589704
## iter 40 value 548.834328
## iter 50 value 543.314725
## iter 60 value 536.531252
## iter 70 value 534.640972
## iter 80 value 533.341632
## iter 90 value 532.333553
## iter 100 value 529.717486
## final value 529.717486
## stopped after 100 iterations
## # weights: 11
## initial value 7915.071497
## iter 10 value 2864.957926
## iter 20 value 1122.326608
## iter 30 value 729.376961
## iter 40 value 658.406492
## iter 50 value 647.853169
## iter 60 value 647.413932
## final value 647.413452
## converged
## # weights: 31
## initial value 4132.240771
## iter 10 value 741.236173
## iter 20 value 645.619998
## iter 30 value 605.618490
## iter 40 value 593.108102
## iter 50 value 590.334088
## iter 60 value 589.098885
## iter 70 value 585.136576
## iter 80 value 580.352358
## iter 90 value 579.296601
## iter 100 value 579.159715
## final value 579.159715
## stopped after 100 iterations
## # weights: 51
## initial value 6278.856332
## iter 10 value 849.649033
## iter 20 value 708.560455
## iter 30 value 660.001609
## iter 40 value 639.760493
## iter 50 value 616.414306
## iter 60 value 590.741098
## iter 70 value 574.838050
## iter 80 value 567.122268
## iter 90 value 564.477966
## iter 100 value 563.466410
## final value 563.466410
## stopped after 100 iterations
## # weights: 11
## initial value 8961.372133
## iter 10 value 2064.992311
## iter 20 value 741.902014
## iter 30 value 656.884862
## iter 40 value 635.586670
## iter 50 value 628.079019
## iter 60 value 627.450175
## iter 70 value 627.051693
## iter 80 value 626.923469
## iter 90 value 626.918007
## final value 626.908153
## converged
## # weights: 31
## initial value 8617.744691
## iter 10 value 926.040912
## iter 20 value 674.245675
## iter 30 value 589.144345
## iter 40 value 580.047908
## iter 50 value 577.740774
## iter 60 value 575.643072
## iter 70 value 573.774007
## iter 80 value 573.412351
## iter 90 value 572.527708
## iter 100 value 570.660851
## final value 570.660851
## stopped after 100 iterations
## # weights: 51
## initial value 4997.573284
## iter 10 value 660.569385
## iter 20 value 580.691548
## iter 30 value 562.171364
## iter 40 value 549.054636
## iter 50 value 543.065733
## iter 60 value 537.782666
## iter 70 value 533.051619
## iter 80 value 529.364318
## iter 90 value 527.980226
## iter 100 value 527.615348
## final value 527.615348
## stopped after 100 iterations
## # weights: 11
## initial value 10393.682630
## iter 10 value 1118.416973
## iter 20 value 853.783303
## iter 30 value 811.771165
## iter 40 value 784.453349
## iter 50 value 696.147412
## iter 60 value 678.514717
## iter 70 value 671.493840
## iter 80 value 670.203651
## iter 90 value 669.545645
## iter 100 value 669.274155
## final value 669.274155
## stopped after 100 iterations
## # weights: 31
## initial value 5862.695992
## iter 10 value 839.605209
## iter 20 value 724.893773
## iter 30 value 663.665104
## iter 40 value 639.229775
## iter 50 value 628.148487
## iter 60 value 619.774810
## iter 70 value 618.437653
## iter 80 value 617.707509
## iter 90 value 615.593619
## iter 100 value 614.047324
## final value 614.047324
## stopped after 100 iterations
## # weights: 51
## initial value 4185.580857
## iter 10 value 806.941136
## iter 20 value 661.865279
## iter 30 value 618.296180
## iter 40 value 605.224838
## iter 50 value 596.823525
## iter 60 value 585.393611
## iter 70 value 577.481805
## iter 80 value 573.797802
## iter 90 value 571.901169
## iter 100 value 570.064554
## final value 570.064554
## stopped after 100 iterations
## # weights: 11
## initial value 7386.545064
## iter 10 value 2334.478728
## iter 20 value 1031.195318
## iter 30 value 720.478919
## iter 40 value 706.426047
## iter 50 value 689.215843
## final value 688.830846
## converged
## # weights: 31
## initial value 4189.123580
## iter 10 value 915.719134
## iter 20 value 747.209850
## iter 30 value 682.258045
## iter 40 value 667.357343
## iter 50 value 658.055143
## iter 60 value 651.474569
## iter 70 value 648.720087
## iter 80 value 644.777474
## iter 90 value 643.233504
## iter 100 value 642.153243
## final value 642.153243
## stopped after 100 iterations
## # weights: 51
## initial value 7179.772059
## iter 10 value 711.461249
## iter 20 value 639.486116
## iter 30 value 624.000106
## iter 40 value 614.319939
## iter 50 value 606.966077
## iter 60 value 604.123560
## iter 70 value 600.572980
## iter 80 value 598.865269
## iter 90 value 598.549481
## iter 100 value 598.378843
## final value 598.378843
## stopped after 100 iterations
## # weights: 11
## initial value 4921.998977
## iter 10 value 1157.293042
## iter 20 value 776.348120
## iter 30 value 677.010601
## iter 40 value 670.406417
## iter 50 value 669.598800
## iter 60 value 669.463674
## iter 70 value 669.308078
## iter 80 value 669.304565
## iter 90 value 669.296721
## final value 669.296690
## converged
## # weights: 31
## initial value 6541.560697
## iter 10 value 766.330143
## iter 20 value 654.187606
## iter 30 value 626.915338
## iter 40 value 621.117770
## iter 50 value 619.407818
## iter 60 value 618.158256
## iter 70 value 618.052556
## iter 80 value 617.952962
## iter 90 value 617.791626
## iter 100 value 617.756821
## final value 617.756821
## stopped after 100 iterations
## # weights: 51
## initial value 8277.369058
## iter 10 value 707.992427
## iter 20 value 634.392286
## iter 30 value 609.897846
## iter 40 value 598.961981
## iter 50 value 588.360805
## iter 60 value 576.757629
## iter 70 value 571.405998
## iter 80 value 569.885192
## iter 90 value 569.257097
## iter 100 value 569.155922
## final value 569.155922
## stopped after 100 iterations
## # weights: 11
## initial value 7447.005776
## iter 10 value 905.980748
## iter 20 value 792.247409
## iter 30 value 784.675611
## iter 40 value 704.085757
## iter 50 value 652.497915
## iter 60 value 644.315901
## iter 70 value 643.388587
## iter 80 value 642.445959
## iter 90 value 642.395737
## iter 100 value 642.307545
## final value 642.307545
## stopped after 100 iterations
## # weights: 31
## initial value 6386.690244
## iter 10 value 962.938694
## iter 20 value 686.149407
## iter 30 value 630.537078
## iter 40 value 609.003586
## iter 50 value 602.279625
## iter 60 value 598.683760
## iter 70 value 598.238142
## iter 80 value 598.126280
## iter 90 value 597.922375
## iter 100 value 597.881746
## final value 597.881746
## stopped after 100 iterations
## # weights: 51
## initial value 4350.594459
## iter 10 value 692.335281
## iter 20 value 599.663930
## iter 30 value 574.455689
## iter 40 value 563.054564
## iter 50 value 556.537870
## iter 60 value 551.966734
## iter 70 value 546.236614
## iter 80 value 539.761642
## iter 90 value 537.639221
## iter 100 value 536.011350
## final value 536.011350
## stopped after 100 iterations
## # weights: 11
## initial value 9555.005353
## iter 10 value 2874.866257
## iter 20 value 1479.174760
## iter 30 value 1019.216805
## iter 40 value 779.504452
## iter 50 value 678.780829
## iter 60 value 663.417486
## iter 70 value 662.435799
## final value 662.301741
## converged
## # weights: 31
## initial value 6080.831650
## iter 10 value 925.762578
## iter 20 value 731.187047
## iter 30 value 637.429815
## iter 40 value 620.137284
## iter 50 value 608.817153
## iter 60 value 601.215471
## iter 70 value 597.501104
## iter 80 value 592.533808
## iter 90 value 589.166259
## iter 100 value 586.365265
## final value 586.365265
## stopped after 100 iterations
## # weights: 51
## initial value 7107.540409
## iter 10 value 710.707402
## iter 20 value 613.829852
## iter 30 value 599.847280
## iter 40 value 584.730898
## iter 50 value 581.031809
## iter 60 value 577.157634
## iter 70 value 572.180393
## iter 80 value 569.255905
## iter 90 value 564.519373
## iter 100 value 563.023036
## final value 563.023036
## stopped after 100 iterations
## # weights: 11
## initial value 5489.756532
## iter 10 value 1506.301186
## iter 20 value 881.060681
## iter 30 value 661.371858
## iter 40 value 645.376490
## iter 50 value 643.413363
## iter 60 value 642.759088
## iter 70 value 642.392414
## iter 80 value 642.369630
## iter 90 value 642.352745
## final value 642.352730
## converged
## # weights: 31
## initial value 4457.058727
## iter 10 value 657.360395
## iter 20 value 594.703735
## iter 30 value 583.578020
## iter 40 value 577.353063
## iter 50 value 574.160723
## iter 60 value 573.884435
## iter 70 value 573.706702
## iter 80 value 573.491817
## iter 90 value 573.078823
## iter 100 value 572.950315
## final value 572.950315
## stopped after 100 iterations
## # weights: 51
## initial value 7338.936849
## iter 10 value 656.720004
## iter 20 value 595.903773
## iter 30 value 576.183815
## iter 40 value 569.073138
## iter 50 value 559.548313
## iter 60 value 555.088904
## iter 70 value 550.181914
## iter 80 value 548.236298
## iter 90 value 547.406507
## iter 100 value 546.051366
## final value 546.051366
## stopped after 100 iterations
## # weights: 11
## initial value 8083.707103
## iter 10 value 1115.936270
## iter 20 value 868.490762
## iter 30 value 814.189419
## iter 40 value 789.624069
## iter 50 value 699.412691
## iter 60 value 666.624376
## iter 70 value 657.198132
## iter 80 value 656.345662
## iter 90 value 655.831601
## iter 100 value 655.742309
## final value 655.742309
## stopped after 100 iterations
## # weights: 31
## initial value 6805.262916
## iter 10 value 872.710382
## iter 20 value 685.368211
## iter 30 value 640.541825
## iter 40 value 628.635528
## iter 50 value 626.106424
## iter 60 value 623.343043
## iter 70 value 622.627989
## iter 80 value 621.597285
## iter 90 value 619.702489
## iter 100 value 615.854702
## final value 615.854702
## stopped after 100 iterations
## # weights: 51
## initial value 4212.028513
## iter 10 value 682.093764
## iter 20 value 616.146475
## iter 30 value 596.067203
## iter 40 value 583.327190
## iter 50 value 578.566674
## iter 60 value 575.839711
## iter 70 value 573.644136
## iter 80 value 569.438349
## iter 90 value 567.127209
## iter 100 value 565.813319
## final value 565.813319
## stopped after 100 iterations
## # weights: 11
## initial value 5012.009553
## iter 10 value 1481.566627
## iter 20 value 831.095878
## iter 30 value 751.472580
## iter 40 value 682.345090
## iter 50 value 675.602815
## iter 60 value 674.275235
## final value 674.240975
## converged
## # weights: 31
## initial value 9048.950629
## iter 10 value 925.375414
## iter 20 value 745.455651
## iter 30 value 701.755294
## iter 40 value 682.129359
## iter 50 value 661.900605
## iter 60 value 653.048274
## iter 70 value 640.134362
## iter 80 value 628.215448
## iter 90 value 625.479893
## iter 100 value 625.079044
## final value 625.079044
## stopped after 100 iterations
## # weights: 51
## initial value 4897.091897
## iter 10 value 675.331803
## iter 20 value 618.321985
## iter 30 value 606.062431
## iter 40 value 600.486252
## iter 50 value 596.214884
## iter 60 value 594.609895
## iter 70 value 593.827484
## iter 80 value 591.835713
## iter 90 value 590.022077
## iter 100 value 588.450307
## final value 588.450307
## stopped after 100 iterations
## # weights: 11
## initial value 7098.529044
## iter 10 value 4074.764966
## iter 20 value 817.048065
## iter 30 value 756.661368
## iter 40 value 731.889353
## iter 50 value 666.530295
## iter 60 value 659.222254
## iter 70 value 656.133420
## iter 80 value 655.808598
## iter 90 value 655.789514
## final value 655.779587
## converged
## # weights: 31
## initial value 7514.443106
## iter 10 value 817.512526
## iter 20 value 668.415266
## iter 30 value 635.748182
## iter 40 value 630.922609
## iter 50 value 626.868143
## iter 60 value 624.693632
## iter 70 value 621.353312
## iter 80 value 616.585340
## iter 90 value 608.832720
## iter 100 value 602.717046
## final value 602.717046
## stopped after 100 iterations
## # weights: 51
## initial value 4280.474170
## iter 10 value 648.733255
## iter 20 value 600.696012
## iter 30 value 584.395491
## iter 40 value 575.284671
## iter 50 value 569.477181
## iter 60 value 568.121256
## iter 70 value 567.661871
## iter 80 value 567.186719
## iter 90 value 566.348292
## iter 100 value 565.393211
## final value 565.393211
## stopped after 100 iterations
## # weights: 11
## initial value 10629.016865
## iter 10 value 2912.335163
## iter 20 value 2853.911353
## final value 2853.910405
## converged
## # weights: 31
## initial value 8825.984590
## iter 10 value 1374.258916
## iter 20 value 831.213763
## iter 30 value 687.930794
## iter 40 value 651.719606
## iter 50 value 631.125177
## iter 60 value 622.619285
## iter 70 value 621.229715
## iter 80 value 619.346107
## iter 90 value 614.788033
## iter 100 value 612.140733
## final value 612.140733
## stopped after 100 iterations
## # weights: 51
## initial value 5837.847656
## iter 10 value 706.973705
## iter 20 value 637.968513
## iter 30 value 607.080548
## iter 40 value 589.340980
## iter 50 value 581.156828
## iter 60 value 575.728351
## iter 70 value 574.148137
## iter 80 value 571.034967
## iter 90 value 569.534661
## iter 100 value 567.888479
## final value 567.888479
## stopped after 100 iterations
## # weights: 11
## initial value 5504.484688
## iter 10 value 1545.795131
## iter 20 value 812.389280
## iter 30 value 760.478798
## iter 40 value 690.256808
## iter 50 value 681.634539
## iter 60 value 681.092327
## final value 681.089157
## converged
## # weights: 31
## initial value 6814.232095
## iter 10 value 807.936972
## iter 20 value 694.869409
## iter 30 value 652.899169
## iter 40 value 635.197169
## iter 50 value 625.182545
## iter 60 value 624.215703
## iter 70 value 623.437382
## iter 80 value 622.254922
## iter 90 value 621.317700
## final value 621.274370
## converged
## # weights: 51
## initial value 10663.860575
## iter 10 value 831.714013
## iter 20 value 668.322206
## iter 30 value 624.448296
## iter 40 value 611.958747
## iter 50 value 606.277328
## iter 60 value 601.259643
## iter 70 value 599.295001
## iter 80 value 596.950960
## iter 90 value 596.068991
## iter 100 value 595.617356
## final value 595.617356
## stopped after 100 iterations
## # weights: 11
## initial value 9960.787973
## iter 10 value 2644.037365
## iter 20 value 2628.462987
## final value 2628.436604
## converged
## # weights: 31
## initial value 8056.262837
## iter 10 value 964.667307
## iter 20 value 727.960075
## iter 30 value 686.880915
## iter 40 value 642.254464
## iter 50 value 627.398245
## iter 60 value 624.955240
## iter 70 value 623.246254
## iter 80 value 617.412063
## iter 90 value 610.265557
## iter 100 value 606.751186
## final value 606.751186
## stopped after 100 iterations
## # weights: 51
## initial value 7229.316958
## iter 10 value 660.257956
## iter 20 value 606.199981
## iter 30 value 595.164764
## iter 40 value 590.845025
## iter 50 value 586.387195
## iter 60 value 582.624222
## iter 70 value 581.523627
## iter 80 value 581.158564
## iter 90 value 580.680055
## iter 100 value 579.373796
## final value 579.373796
## stopped after 100 iterations
## # weights: 11
## initial value 8574.775284
## iter 10 value 1681.305558
## iter 20 value 776.721093
## iter 30 value 708.380578
## iter 40 value 672.381766
## iter 50 value 657.614356
## iter 60 value 656.527003
## iter 70 value 655.238193
## iter 80 value 655.136642
## iter 90 value 655.125791
## final value 655.125760
## converged
## # weights: 31
## initial value 6946.001728
## iter 10 value 752.340612
## iter 20 value 665.391845
## iter 30 value 633.961332
## iter 40 value 613.383947
## iter 50 value 608.795118
## iter 60 value 605.424972
## iter 70 value 604.164442
## iter 80 value 602.814821
## iter 90 value 601.927201
## iter 100 value 600.626431
## final value 600.626431
## stopped after 100 iterations
## # weights: 51
## initial value 3978.139345
## iter 10 value 698.896664
## iter 20 value 609.933218
## iter 30 value 590.981642
## iter 40 value 578.403509
## iter 50 value 572.985309
## iter 60 value 568.102247
## iter 70 value 563.620306
## iter 80 value 561.502844
## iter 90 value 560.278013
## iter 100 value 559.475012
## final value 559.475012
## stopped after 100 iterations
## # weights: 11
## initial value 4483.423870
## iter 10 value 769.857996
## iter 20 value 700.296257
## iter 30 value 676.934107
## iter 40 value 676.285651
## final value 676.275096
## converged
## # weights: 31
## initial value 11384.738532
## iter 10 value 1159.151560
## iter 20 value 753.503980
## iter 30 value 666.308969
## iter 40 value 633.980381
## iter 50 value 622.998950
## iter 60 value 614.952134
## iter 70 value 613.066879
## iter 80 value 612.853412
## iter 90 value 612.759915
## final value 612.757599
## converged
## # weights: 51
## initial value 6492.346495
## iter 10 value 774.272243
## iter 20 value 663.580459
## iter 30 value 629.832286
## iter 40 value 621.628432
## iter 50 value 613.174421
## iter 60 value 606.161492
## iter 70 value 603.570238
## iter 80 value 601.496028
## iter 90 value 599.621397
## iter 100 value 598.277049
## final value 598.277049
## stopped after 100 iterations
## # weights: 11
## initial value 8533.105918
## iter 10 value 846.480714
## iter 20 value 740.647517
## iter 30 value 689.554848
## iter 40 value 662.784657
## iter 50 value 655.967745
## iter 60 value 655.565667
## iter 70 value 655.164196
## final value 655.158253
## converged
## # weights: 31
## initial value 8585.272449
## iter 10 value 918.198114
## iter 20 value 749.065847
## iter 30 value 711.928200
## iter 40 value 658.265303
## iter 50 value 637.005012
## iter 60 value 631.316400
## iter 70 value 626.218527
## iter 80 value 615.072940
## iter 90 value 612.909048
## iter 100 value 610.753857
## final value 610.753857
## stopped after 100 iterations
## # weights: 51
## initial value 3898.229636
## iter 10 value 698.526313
## iter 20 value 632.697396
## iter 30 value 605.288880
## iter 40 value 588.800558
## iter 50 value 579.163779
## iter 60 value 568.839375
## iter 70 value 562.104070
## iter 80 value 558.826213
## iter 90 value 556.567002
## iter 100 value 555.572339
## final value 555.572339
## stopped after 100 iterations
## # weights: 11
## initial value 11009.236371
## iter 10 value 2666.543849
## iter 20 value 2666.186419
## final value 2666.186357
## converged
## # weights: 31
## initial value 7213.971891
## iter 10 value 848.032951
## iter 20 value 729.471151
## iter 30 value 685.621561
## iter 40 value 648.222175
## iter 50 value 639.713380
## iter 60 value 630.322134
## iter 70 value 629.351328
## iter 80 value 628.816873
## iter 90 value 628.773523
## iter 100 value 628.752528
## final value 628.752528
## stopped after 100 iterations
## # weights: 51
## initial value 4865.292040
## iter 10 value 700.606862
## iter 20 value 630.468732
## iter 30 value 604.234768
## iter 40 value 588.866199
## iter 50 value 578.738604
## iter 60 value 572.639271
## iter 70 value 569.183056
## iter 80 value 567.271673
## iter 90 value 566.526620
## iter 100 value 566.087855
## final value 566.087855
## stopped after 100 iterations
## # weights: 11
## initial value 5679.932884
## iter 10 value 1610.890310
## iter 20 value 996.700073
## iter 30 value 738.192639
## iter 40 value 689.225580
## iter 50 value 687.742301
## final value 687.606228
## converged
## # weights: 31
## initial value 9622.183528
## iter 10 value 1154.416125
## iter 20 value 836.875493
## iter 30 value 720.094303
## iter 40 value 676.491171
## iter 50 value 658.263478
## iter 60 value 640.560450
## iter 70 value 637.194928
## iter 80 value 635.565004
## iter 90 value 634.238123
## iter 100 value 634.215066
## final value 634.215066
## stopped after 100 iterations
## # weights: 51
## initial value 6300.934710
## iter 10 value 695.661449
## iter 20 value 646.601862
## iter 30 value 627.946785
## iter 40 value 617.044969
## iter 50 value 609.939538
## iter 60 value 607.429900
## iter 70 value 605.126659
## iter 80 value 603.166307
## iter 90 value 602.790492
## iter 100 value 600.991025
## final value 600.991025
## stopped after 100 iterations
## # weights: 11
## initial value 6121.078663
## iter 10 value 2624.871227
## iter 20 value 770.598254
## iter 30 value 696.434283
## iter 40 value 679.184520
## iter 50 value 669.151019
## iter 60 value 668.254405
## iter 70 value 667.722975
## iter 80 value 667.552041
## iter 90 value 667.535501
## iter 100 value 667.503034
## final value 667.503034
## stopped after 100 iterations
## # weights: 31
## initial value 13209.005555
## iter 10 value 1380.107988
## iter 20 value 706.682611
## iter 30 value 665.511603
## iter 40 value 631.896567
## iter 50 value 621.633995
## iter 60 value 615.286426
## iter 70 value 609.214889
## iter 80 value 607.067495
## iter 90 value 606.894619
## iter 100 value 606.798900
## final value 606.798900
## stopped after 100 iterations
## # weights: 51
## initial value 4113.201924
## iter 10 value 745.544106
## iter 20 value 635.819746
## iter 30 value 619.126532
## iter 40 value 608.077780
## iter 50 value 594.315007
## iter 60 value 584.841328
## iter 70 value 577.066760
## iter 80 value 571.322243
## iter 90 value 568.324017
## iter 100 value 564.594301
## final value 564.594301
## stopped after 100 iterations
## # weights: 11
## initial value 6083.822839
## iter 10 value 1916.351839
## iter 20 value 849.625696
## iter 30 value 820.613994
## iter 40 value 803.989874
## iter 50 value 699.125103
## iter 60 value 666.499809
## iter 70 value 653.174067
## iter 80 value 650.942819
## iter 90 value 650.295665
## iter 100 value 649.568305
## final value 649.568305
## stopped after 100 iterations
## # weights: 31
## initial value 12072.111047
## iter 10 value 714.684364
## iter 20 value 658.204920
## iter 30 value 625.316024
## iter 40 value 604.983525
## iter 50 value 595.134466
## iter 60 value 588.760679
## iter 70 value 587.001767
## iter 80 value 585.128637
## iter 90 value 584.385698
## iter 100 value 580.297998
## final value 580.297998
## stopped after 100 iterations
## # weights: 51
## initial value 6358.045850
## iter 10 value 841.560076
## iter 20 value 656.845070
## iter 30 value 633.208080
## iter 40 value 610.309828
## iter 50 value 600.680344
## iter 60 value 590.869200
## iter 70 value 583.450083
## iter 80 value 576.138859
## iter 90 value 574.226473
## iter 100 value 572.951987
## final value 572.951987
## stopped after 100 iterations
## # weights: 11
## initial value 4778.112974
## iter 10 value 1180.106776
## iter 20 value 722.135393
## iter 30 value 682.740595
## iter 40 value 671.117339
## final value 671.079652
## converged
## # weights: 31
## initial value 6730.994519
## iter 10 value 827.561725
## iter 20 value 696.369710
## iter 30 value 668.050531
## iter 40 value 654.050462
## iter 50 value 636.175018
## iter 60 value 625.517388
## iter 70 value 621.247076
## iter 80 value 616.313478
## iter 90 value 608.563653
## iter 100 value 607.831043
## final value 607.831043
## stopped after 100 iterations
## # weights: 51
## initial value 4888.844538
## iter 10 value 728.931282
## iter 20 value 658.545931
## iter 30 value 636.438871
## iter 40 value 620.426867
## iter 50 value 607.869662
## iter 60 value 600.521777
## iter 70 value 599.038055
## iter 80 value 596.739589
## iter 90 value 595.185056
## iter 100 value 594.473441
## final value 594.473441
## stopped after 100 iterations
## # weights: 11
## initial value 6603.667237
## iter 10 value 994.162295
## iter 20 value 775.170689
## iter 30 value 735.682027
## iter 40 value 673.000662
## iter 50 value 656.630108
## iter 60 value 652.771226
## iter 70 value 649.944731
## iter 80 value 649.882499
## iter 90 value 649.541176
## iter 100 value 649.483704
## final value 649.483704
## stopped after 100 iterations
## # weights: 31
## initial value 3925.366538
## iter 10 value 656.748123
## iter 20 value 621.925316
## iter 30 value 608.449995
## iter 40 value 601.974897
## iter 50 value 593.738376
## iter 60 value 590.780303
## iter 70 value 588.159491
## iter 80 value 587.214135
## iter 90 value 584.179014
## iter 100 value 583.255115
## final value 583.255115
## stopped after 100 iterations
## # weights: 51
## initial value 5110.558384
## iter 10 value 862.441826
## iter 20 value 652.954872
## iter 30 value 623.872438
## iter 40 value 599.137209
## iter 50 value 591.394444
## iter 60 value 589.280965
## iter 70 value 587.408499
## iter 80 value 584.837673
## iter 90 value 581.549223
## iter 100 value 580.399616
## final value 580.399616
## stopped after 100 iterations
## # weights: 11
## initial value 5362.794503
## iter 10 value 1228.676351
## iter 20 value 679.526599
## iter 30 value 654.076155
## iter 40 value 644.748421
## iter 50 value 644.512750
## iter 60 value 644.498776
## final value 644.493056
## converged
## # weights: 31
## initial value 9856.496265
## iter 10 value 900.327495
## iter 20 value 689.655467
## iter 30 value 619.518915
## iter 40 value 597.450560
## iter 50 value 583.292432
## iter 60 value 578.822180
## iter 70 value 577.836436
## iter 80 value 577.252524
## iter 90 value 576.403765
## iter 100 value 575.970307
## final value 575.970307
## stopped after 100 iterations
## # weights: 51
## initial value 7511.848778
## iter 10 value 693.106188
## iter 20 value 613.782476
## iter 30 value 587.055926
## iter 40 value 577.321478
## iter 50 value 570.306018
## iter 60 value 557.408675
## iter 70 value 552.544185
## iter 80 value 548.461956
## iter 90 value 546.959729
## iter 100 value 544.581062
## final value 544.581062
## stopped after 100 iterations
## # weights: 11
## initial value 5916.330008
## iter 10 value 1070.455024
## iter 20 value 725.380451
## iter 30 value 685.296027
## iter 40 value 663.251711
## iter 50 value 662.483573
## final value 662.483563
## converged
## # weights: 31
## initial value 8613.628778
## iter 10 value 1068.579201
## iter 20 value 744.658287
## iter 30 value 657.294503
## iter 40 value 633.629777
## iter 50 value 627.521799
## iter 60 value 621.965515
## iter 70 value 617.293765
## iter 80 value 613.936172
## iter 90 value 613.038228
## iter 100 value 610.571732
## final value 610.571732
## stopped after 100 iterations
## # weights: 51
## initial value 8481.905248
## iter 10 value 781.918267
## iter 20 value 671.982137
## iter 30 value 621.614472
## iter 40 value 607.704942
## iter 50 value 600.155505
## iter 60 value 594.336709
## iter 70 value 588.818418
## iter 80 value 586.599610
## iter 90 value 584.263205
## iter 100 value 579.845789
## final value 579.845789
## stopped after 100 iterations
## # weights: 11
## initial value 4589.684839
## iter 10 value 902.545370
## iter 20 value 828.086253
## iter 30 value 810.651064
## iter 40 value 808.091331
## iter 50 value 807.150760
## iter 60 value 789.499083
## iter 70 value 742.538368
## iter 80 value 667.135875
## iter 90 value 651.985078
## iter 100 value 646.012426
## final value 646.012426
## stopped after 100 iterations
## # weights: 31
## initial value 5241.850840
## iter 10 value 682.595744
## iter 20 value 622.571144
## iter 30 value 603.488821
## iter 40 value 595.774964
## iter 50 value 581.899288
## iter 60 value 577.158322
## iter 70 value 576.937538
## iter 80 value 576.668599
## iter 90 value 576.304915
## iter 100 value 575.987383
## final value 575.987383
## stopped after 100 iterations
## # weights: 51
## initial value 5413.983042
## iter 10 value 700.185579
## iter 20 value 609.737131
## iter 30 value 584.861775
## iter 40 value 573.864564
## iter 50 value 565.444199
## iter 60 value 561.475063
## iter 70 value 555.930399
## iter 80 value 551.228633
## iter 90 value 549.086428
## iter 100 value 547.821946
## final value 547.821946
## stopped after 100 iterations
## # weights: 11
## initial value 4717.294111
## iter 10 value 1543.484357
## iter 20 value 840.752013
## iter 30 value 813.097112
## iter 40 value 769.186019
## iter 50 value 696.318539
## iter 60 value 673.439187
## iter 70 value 668.936163
## iter 80 value 666.237657
## iter 90 value 666.100130
## iter 100 value 665.852958
## final value 665.852958
## stopped after 100 iterations
## # weights: 31
## initial value 7333.410109
## iter 10 value 724.972371
## iter 20 value 659.702705
## iter 30 value 627.380866
## iter 40 value 615.478481
## iter 50 value 612.666939
## iter 60 value 611.455063
## iter 70 value 610.522892
## iter 80 value 609.144549
## iter 90 value 607.479587
## iter 100 value 606.862146
## final value 606.862146
## stopped after 100 iterations
## # weights: 51
## initial value 5915.042650
## iter 10 value 755.912067
## iter 20 value 631.985474
## iter 30 value 600.757017
## iter 40 value 589.751615
## iter 50 value 580.972797
## iter 60 value 572.043947
## iter 70 value 569.556229
## iter 80 value 567.008530
## iter 90 value 558.402985
## iter 100 value 552.925885
## final value 552.925885
## stopped after 100 iterations
## # weights: 11
## initial value 3902.844788
## iter 10 value 1614.718971
## iter 20 value 886.732613
## iter 30 value 800.886331
## iter 40 value 700.473846
## iter 50 value 686.550928
## iter 60 value 685.832487
## final value 685.831686
## converged
## # weights: 31
## initial value 3265.708523
## iter 10 value 765.205090
## iter 20 value 681.912700
## iter 30 value 653.399887
## iter 40 value 644.023883
## iter 50 value 631.889017
## iter 60 value 625.872221
## iter 70 value 625.346965
## iter 80 value 624.740334
## iter 90 value 624.677394
## final value 624.677313
## converged
## # weights: 51
## initial value 5942.451756
## iter 10 value 989.891677
## iter 20 value 833.395745
## iter 30 value 772.159748
## iter 40 value 683.784897
## iter 50 value 638.484868
## iter 60 value 628.696863
## iter 70 value 616.113280
## iter 80 value 610.709933
## iter 90 value 607.233206
## iter 100 value 606.253501
## final value 606.253501
## stopped after 100 iterations
## # weights: 11
## initial value 5643.214680
## iter 10 value 941.922211
## iter 20 value 864.752283
## iter 30 value 852.914244
## iter 40 value 830.166605
## iter 50 value 821.893087
## iter 60 value 773.705722
## iter 70 value 720.806977
## iter 80 value 674.257370
## iter 90 value 670.342605
## iter 100 value 666.595086
## final value 666.595086
## stopped after 100 iterations
## # weights: 31
## initial value 4046.781303
## iter 10 value 816.665255
## iter 20 value 703.255615
## iter 30 value 662.806929
## iter 40 value 645.826106
## iter 50 value 640.788648
## iter 60 value 636.898368
## iter 70 value 636.490338
## iter 80 value 636.206695
## iter 90 value 632.014507
## iter 100 value 616.126964
## final value 616.126964
## stopped after 100 iterations
## # weights: 51
## initial value 7680.754343
## iter 10 value 687.280209
## iter 20 value 616.431695
## iter 30 value 596.013920
## iter 40 value 583.360246
## iter 50 value 574.496922
## iter 60 value 571.878719
## iter 70 value 568.538222
## iter 80 value 564.137073
## iter 90 value 561.168361
## iter 100 value 560.110046
## final value 560.110046
## stopped after 100 iterations
## # weights: 11
## initial value 7233.446602
## iter 10 value 2542.226277
## final value 2542.225540
## converged
## # weights: 31
## initial value 7729.737911
## iter 10 value 752.925782
## iter 20 value 649.956112
## iter 30 value 634.701025
## iter 40 value 625.776379
## iter 50 value 621.873175
## iter 60 value 621.139859
## iter 70 value 620.708237
## iter 80 value 620.111472
## iter 90 value 619.089013
## iter 100 value 618.999354
## final value 618.999354
## stopped after 100 iterations
## # weights: 51
## initial value 8059.718284
## iter 10 value 767.315019
## iter 20 value 638.933560
## iter 30 value 616.024366
## iter 40 value 587.302965
## iter 50 value 573.254014
## iter 60 value 565.439236
## iter 70 value 562.748832
## iter 80 value 562.083009
## iter 90 value 561.791475
## iter 100 value 561.540384
## final value 561.540384
## stopped after 100 iterations
## # weights: 11
## initial value 7221.097307
## iter 10 value 2232.690372
## iter 20 value 1084.474787
## iter 30 value 770.651480
## iter 40 value 719.421290
## iter 50 value 694.185797
## iter 60 value 693.054169
## final value 692.960170
## converged
## # weights: 31
## initial value 3754.255632
## iter 10 value 958.034310
## iter 20 value 769.150523
## iter 30 value 684.913510
## iter 40 value 664.736465
## iter 50 value 645.302193
## iter 60 value 634.795991
## iter 70 value 630.763836
## iter 80 value 627.808903
## iter 90 value 624.481257
## iter 100 value 622.965166
## final value 622.965166
## stopped after 100 iterations
## # weights: 51
## initial value 8710.184894
## iter 10 value 1393.767666
## iter 20 value 968.482932
## iter 30 value 803.924129
## iter 40 value 657.373646
## iter 50 value 631.090759
## iter 60 value 618.770541
## iter 70 value 612.727972
## iter 80 value 608.450803
## iter 90 value 606.184155
## iter 100 value 605.631568
## final value 605.631568
## stopped after 100 iterations
## # weights: 11
## initial value 4818.618875
## iter 10 value 931.017470
## iter 20 value 879.382693
## iter 30 value 872.277427
## iter 40 value 841.672471
## iter 50 value 838.340801
## iter 60 value 835.209259
## iter 70 value 747.680281
## iter 80 value 684.592897
## iter 90 value 677.735602
## iter 100 value 675.086399
## final value 675.086399
## stopped after 100 iterations
## # weights: 31
## initial value 5131.327749
## iter 10 value 786.106812
## iter 20 value 665.098722
## iter 30 value 633.196614
## iter 40 value 627.929003
## iter 50 value 624.544470
## iter 60 value 623.963137
## iter 70 value 623.747992
## iter 80 value 623.622419
## iter 90 value 623.515995
## iter 100 value 623.101094
## final value 623.101094
## stopped after 100 iterations
## # weights: 51
## initial value 5520.252335
## iter 10 value 836.188975
## iter 20 value 678.536961
## iter 30 value 623.808213
## iter 40 value 607.116907
## iter 50 value 595.926699
## iter 60 value 591.013019
## iter 70 value 584.723295
## iter 80 value 581.373924
## iter 90 value 579.528228
## iter 100 value 578.461029
## final value 578.461029
## stopped after 100 iterations
## # weights: 11
## initial value 7557.421355
## iter 10 value 2414.914193
## iter 20 value 2394.657044
## final value 2394.609001
## converged
## # weights: 31
## initial value 8495.873132
## iter 10 value 1033.939594
## iter 20 value 750.765332
## iter 30 value 662.454110
## iter 40 value 636.944558
## iter 50 value 629.064005
## iter 60 value 624.964315
## iter 70 value 624.057173
## iter 80 value 621.812613
## iter 90 value 619.638032
## iter 100 value 618.560854
## final value 618.560854
## stopped after 100 iterations
## # weights: 51
## initial value 7717.314630
## iter 10 value 709.874485
## iter 20 value 626.612546
## iter 30 value 605.088836
## iter 40 value 593.267601
## iter 50 value 584.821401
## iter 60 value 581.945538
## iter 70 value 580.504432
## iter 80 value 578.619201
## iter 90 value 576.525573
## iter 100 value 573.625694
## final value 573.625694
## stopped after 100 iterations
## # weights: 11
## initial value 4207.417639
## iter 10 value 1423.756453
## iter 20 value 966.456889
## iter 30 value 715.241011
## iter 40 value 685.104806
## final value 684.714779
## converged
## # weights: 31
## initial value 6782.880245
## iter 10 value 827.820504
## iter 20 value 731.452193
## iter 30 value 692.452456
## iter 40 value 662.758663
## iter 50 value 652.813559
## iter 60 value 632.389244
## iter 70 value 627.242951
## iter 80 value 624.571196
## iter 90 value 622.195734
## iter 100 value 619.191292
## final value 619.191292
## stopped after 100 iterations
## # weights: 51
## initial value 6115.800136
## iter 10 value 731.687311
## iter 20 value 652.721135
## iter 30 value 631.003062
## iter 40 value 623.172988
## iter 50 value 617.174010
## iter 60 value 607.592519
## iter 70 value 599.879719
## iter 80 value 597.830247
## iter 90 value 596.837332
## iter 100 value 596.689502
## final value 596.689502
## stopped after 100 iterations
## # weights: 11
## initial value 5633.650148
## iter 10 value 1627.000682
## iter 20 value 942.429593
## iter 30 value 731.145746
## iter 40 value 679.837272
## iter 50 value 670.183388
## iter 60 value 664.535206
## iter 70 value 664.224621
## iter 80 value 663.814623
## iter 90 value 663.694113
## iter 90 value 663.694109
## final value 663.693976
## converged
## # weights: 31
## initial value 5443.469539
## iter 10 value 740.245775
## iter 20 value 642.030627
## iter 30 value 618.395263
## iter 40 value 607.359507
## iter 50 value 601.041230
## iter 60 value 598.267839
## iter 70 value 597.731774
## iter 80 value 597.140921
## iter 90 value 596.934788
## iter 100 value 596.838160
## final value 596.838160
## stopped after 100 iterations
## # weights: 51
## initial value 5835.586478
## iter 10 value 861.166619
## iter 20 value 641.729654
## iter 30 value 596.697757
## iter 40 value 578.391390
## iter 50 value 572.204021
## iter 60 value 568.806734
## iter 70 value 567.519002
## iter 80 value 566.568355
## iter 90 value 566.323832
## iter 100 value 565.982240
## final value 565.982240
## stopped after 100 iterations
## # weights: 11
## initial value 6297.658098
## iter 10 value 1859.797532
## iter 20 value 814.689366
## iter 30 value 804.831070
## iter 40 value 794.653031
## iter 50 value 715.172532
## iter 60 value 659.085440
## iter 70 value 647.942238
## iter 80 value 646.495787
## iter 90 value 646.057242
## final value 645.989690
## converged
## # weights: 31
## initial value 5361.151658
## iter 10 value 1237.381802
## iter 20 value 678.237877
## iter 30 value 617.615671
## iter 40 value 603.716231
## iter 50 value 600.932237
## iter 60 value 599.753815
## iter 70 value 599.529084
## iter 80 value 599.386772
## iter 90 value 599.271659
## iter 100 value 598.631409
## final value 598.631409
## stopped after 100 iterations
## # weights: 51
## initial value 6025.345408
## iter 10 value 735.303051
## iter 20 value 619.364886
## iter 30 value 588.912869
## iter 40 value 577.639841
## iter 50 value 570.788964
## iter 60 value 564.036654
## iter 70 value 559.661959
## iter 80 value 558.665872
## iter 90 value 557.890092
## iter 100 value 557.358676
## final value 557.358676
## stopped after 100 iterations
## # weights: 11
## initial value 8547.439089
## iter 10 value 2253.943186
## iter 20 value 2121.086217
## iter 30 value 1323.639995
## iter 40 value 717.554053
## iter 50 value 685.695388
## iter 60 value 667.391175
## iter 70 value 663.950366
## final value 663.950037
## converged
## # weights: 31
## initial value 3874.662391
## iter 10 value 1004.989430
## iter 20 value 745.684554
## iter 30 value 681.059764
## iter 40 value 646.925659
## iter 50 value 633.793116
## iter 60 value 623.225097
## iter 70 value 620.821419
## iter 80 value 617.297548
## iter 90 value 616.062675
## iter 100 value 613.951424
## final value 613.951424
## stopped after 100 iterations
## # weights: 51
## initial value 4581.637657
## iter 10 value 671.670681
## iter 20 value 624.104315
## iter 30 value 606.317561
## iter 40 value 597.487895
## iter 50 value 592.837138
## iter 60 value 588.986617
## iter 70 value 587.204783
## iter 80 value 583.972588
## iter 90 value 582.488000
## iter 100 value 581.895321
## final value 581.895321
## stopped after 100 iterations
## # weights: 11
## initial value 5022.993393
## iter 10 value 942.878435
## iter 20 value 656.329720
## iter 30 value 649.352114
## iter 40 value 646.729558
## iter 50 value 646.026749
## iter 60 value 646.024787
## final value 646.021715
## converged
## # weights: 31
## initial value 3691.582431
## iter 10 value 949.945973
## iter 20 value 702.759970
## iter 30 value 650.928420
## iter 40 value 616.241463
## iter 50 value 595.556452
## iter 60 value 592.207082
## iter 70 value 590.981440
## iter 80 value 587.795975
## iter 90 value 582.608212
## iter 100 value 581.179748
## final value 581.179748
## stopped after 100 iterations
## # weights: 51
## initial value 4352.877922
## iter 10 value 1236.085648
## iter 20 value 790.212959
## iter 30 value 659.519787
## iter 40 value 634.386640
## iter 50 value 629.873592
## iter 60 value 626.263227
## iter 70 value 625.372279
## iter 80 value 624.406462
## iter 90 value 621.294986
## iter 100 value 616.955127
## final value 616.955127
## stopped after 100 iterations
## # weights: 11
## initial value 6881.376825
## iter 10 value 1247.315310
## iter 20 value 709.247670
## iter 30 value 675.078170
## iter 40 value 647.335984
## iter 50 value 637.417639
## iter 60 value 636.876800
## iter 70 value 635.630221
## iter 80 value 635.471551
## iter 90 value 635.402709
## iter 100 value 635.352976
## final value 635.352976
## stopped after 100 iterations
## # weights: 31
## initial value 6245.385714
## iter 10 value 786.677326
## iter 20 value 639.137489
## iter 30 value 606.013444
## iter 40 value 593.855074
## iter 50 value 585.589089
## iter 60 value 582.396473
## iter 70 value 581.570339
## iter 80 value 581.145768
## iter 90 value 579.738601
## iter 100 value 579.390746
## final value 579.390746
## stopped after 100 iterations
## # weights: 51
## initial value 8955.683095
## iter 10 value 804.787607
## iter 20 value 635.968401
## iter 30 value 587.455645
## iter 40 value 578.279442
## iter 50 value 568.528747
## iter 60 value 563.288958
## iter 70 value 553.180529
## iter 80 value 546.863653
## iter 90 value 542.734493
## iter 100 value 538.719870
## final value 538.719870
## stopped after 100 iterations
## # weights: 11
## initial value 8196.551945
## iter 10 value 2854.814177
## iter 20 value 1367.077854
## iter 30 value 943.966207
## iter 40 value 751.053403
## iter 50 value 674.236063
## iter 60 value 656.916728
## iter 70 value 656.038311
## final value 655.915872
## converged
## # weights: 31
## initial value 5557.563213
## iter 10 value 732.616683
## iter 20 value 663.624224
## iter 30 value 621.977197
## iter 40 value 608.821462
## iter 50 value 595.473338
## iter 60 value 591.474766
## iter 70 value 591.198936
## final value 591.198531
## converged
## # weights: 51
## initial value 3577.556558
## iter 10 value 777.216407
## iter 20 value 660.486072
## iter 30 value 604.373243
## iter 40 value 589.637454
## iter 50 value 582.978938
## iter 60 value 580.030958
## iter 70 value 576.067400
## iter 80 value 574.611792
## iter 90 value 573.483103
## iter 100 value 573.136955
## final value 573.136955
## stopped after 100 iterations
## # weights: 11
## initial value 7896.860754
## iter 10 value 1644.166583
## iter 20 value 981.817891
## iter 30 value 730.361828
## iter 40 value 636.130893
## iter 50 value 635.692405
## iter 60 value 635.610879
## iter 70 value 635.467717
## iter 80 value 635.453767
## iter 90 value 635.427364
## final value 635.425458
## converged
## # weights: 31
## initial value 4082.728581
## iter 10 value 856.788953
## iter 20 value 701.940925
## iter 30 value 656.504149
## iter 40 value 645.724911
## iter 50 value 641.144012
## iter 60 value 636.134951
## iter 70 value 628.093936
## iter 80 value 621.521897
## iter 90 value 608.154238
## iter 100 value 603.584417
## final value 603.584417
## stopped after 100 iterations
## # weights: 51
## initial value 10120.594594
## iter 10 value 776.102421
## iter 20 value 611.035176
## iter 30 value 568.723676
## iter 40 value 552.213178
## iter 50 value 542.816740
## iter 60 value 538.358226
## iter 70 value 536.082641
## iter 80 value 534.358123
## iter 90 value 530.610732
## iter 100 value 529.756198
## final value 529.756198
## stopped after 100 iterations
## # weights: 11
## initial value 7951.615735
## iter 10 value 2754.864252
## iter 20 value 1551.617669
## iter 30 value 995.773495
## iter 40 value 929.939446
## iter 50 value 867.351695
## iter 60 value 860.763870
## iter 70 value 859.533099
## iter 80 value 859.488034
## iter 90 value 859.281371
## iter 100 value 859.044840
## final value 859.044840
## stopped after 100 iterations
## # weights: 31
## initial value 8252.524089
## iter 10 value 898.384585
## iter 20 value 716.494028
## iter 30 value 677.860243
## iter 40 value 642.786636
## iter 50 value 628.741888
## iter 60 value 625.234993
## iter 70 value 624.472835
## iter 80 value 624.257803
## iter 90 value 623.817986
## iter 100 value 622.813203
## final value 622.813203
## stopped after 100 iterations
## # weights: 51
## initial value 7635.940298
## iter 10 value 684.356995
## iter 20 value 623.604877
## iter 30 value 605.409907
## iter 40 value 591.008083
## iter 50 value 584.877818
## iter 60 value 580.366922
## iter 70 value 576.683976
## iter 80 value 574.238814
## iter 90 value 567.450127
## iter 100 value 561.073319
## final value 561.073319
## stopped after 100 iterations
## # weights: 11
## initial value 4638.650967
## iter 10 value 1140.986699
## iter 20 value 784.262363
## iter 30 value 702.422067
## iter 40 value 691.055791
## final value 690.674180
## converged
## # weights: 31
## initial value 8717.377734
## iter 10 value 1023.837838
## iter 20 value 828.289436
## iter 30 value 730.478069
## iter 40 value 678.437594
## iter 50 value 646.962334
## iter 60 value 639.918794
## iter 70 value 635.059342
## iter 80 value 631.317312
## iter 90 value 629.632294
## iter 100 value 628.470522
## final value 628.470522
## stopped after 100 iterations
## # weights: 51
## initial value 3332.607853
## iter 10 value 693.573438
## iter 20 value 639.788260
## iter 30 value 627.371840
## iter 40 value 619.506277
## iter 50 value 611.352505
## iter 60 value 607.379575
## iter 70 value 605.441314
## iter 80 value 603.091844
## iter 90 value 599.526185
## iter 100 value 597.936581
## final value 597.936581
## stopped after 100 iterations
## # weights: 11
## initial value 4893.214358
## iter 10 value 3268.952317
## iter 20 value 946.408391
## iter 30 value 901.653146
## iter 40 value 868.806179
## iter 50 value 850.178129
## iter 60 value 834.319540
## iter 70 value 767.233121
## iter 80 value 694.538017
## iter 90 value 680.983364
## iter 100 value 673.718739
## final value 673.718739
## stopped after 100 iterations
## # weights: 31
## initial value 3423.197472
## iter 10 value 833.250322
## iter 20 value 691.844688
## iter 30 value 657.386607
## iter 40 value 636.157893
## iter 50 value 630.451942
## iter 60 value 625.369589
## iter 70 value 624.195953
## iter 80 value 622.911889
## iter 90 value 622.068842
## iter 100 value 621.644385
## final value 621.644385
## stopped after 100 iterations
## # weights: 51
## initial value 11481.695962
## iter 10 value 811.793767
## iter 20 value 652.965629
## iter 30 value 623.578182
## iter 40 value 612.246510
## iter 50 value 603.093008
## iter 60 value 593.742053
## iter 70 value 588.405789
## iter 80 value 580.377998
## iter 90 value 579.249122
## iter 100 value 577.947725
## final value 577.947725
## stopped after 100 iterations
## # weights: 11
## initial value 5741.057566
## iter 10 value 1168.455896
## iter 20 value 707.067331
## iter 30 value 647.892781
## iter 40 value 643.540334
## iter 50 value 643.433519
## iter 60 value 643.306380
## final value 643.295706
## converged
## # weights: 31
## initial value 4855.070117
## iter 10 value 741.237890
## iter 20 value 649.755909
## iter 30 value 605.251959
## iter 40 value 585.636912
## iter 50 value 580.259132
## iter 60 value 577.550026
## iter 70 value 577.018157
## iter 80 value 576.228687
## iter 90 value 575.258416
## iter 100 value 574.495511
## final value 574.495511
## stopped after 100 iterations
## # weights: 51
## initial value 15342.029093
## iter 10 value 667.516397
## iter 20 value 589.500871
## iter 30 value 573.253372
## iter 40 value 565.784240
## iter 50 value 560.612020
## iter 60 value 558.580942
## iter 70 value 556.328694
## iter 80 value 552.983583
## iter 90 value 551.026475
## iter 100 value 548.930605
## final value 548.930605
## stopped after 100 iterations
## # weights: 11
## initial value 5885.841720
## iter 10 value 1407.817186
## iter 20 value 859.187440
## iter 30 value 699.713370
## iter 40 value 668.570456
## iter 50 value 663.496602
## final value 663.495428
## converged
## # weights: 31
## initial value 6466.890967
## iter 10 value 925.241447
## iter 20 value 709.695008
## iter 30 value 652.596598
## iter 40 value 622.062078
## iter 50 value 612.445385
## iter 60 value 608.590853
## iter 70 value 607.894440
## iter 80 value 607.741416
## iter 90 value 607.527386
## iter 100 value 604.041371
## final value 604.041371
## stopped after 100 iterations
## # weights: 51
## initial value 6611.063847
## iter 10 value 724.636902
## iter 20 value 646.043475
## iter 30 value 620.376850
## iter 40 value 606.873010
## iter 50 value 594.162866
## iter 60 value 588.125790
## iter 70 value 580.500021
## iter 80 value 577.689142
## iter 90 value 576.395445
## iter 100 value 576.038212
## final value 576.038212
## stopped after 100 iterations
## # weights: 11
## initial value 4147.233417
## iter 10 value 2058.277637
## iter 20 value 803.099715
## iter 30 value 726.556806
## iter 40 value 713.329514
## iter 50 value 657.429327
## iter 60 value 648.711450
## iter 70 value 644.624655
## iter 80 value 643.562625
## iter 90 value 643.510015
## iter 100 value 643.352520
## final value 643.352520
## stopped after 100 iterations
## # weights: 31
## initial value 4036.395778
## iter 10 value 771.362691
## iter 20 value 678.358152
## iter 30 value 631.985056
## iter 40 value 619.603116
## iter 50 value 614.602919
## iter 60 value 612.415703
## iter 70 value 612.119313
## iter 80 value 611.835971
## iter 90 value 611.591136
## iter 100 value 611.419883
## final value 611.419883
## stopped after 100 iterations
## # weights: 51
## initial value 4885.192454
## iter 10 value 727.247327
## iter 20 value 611.967144
## iter 30 value 578.432974
## iter 40 value 566.932264
## iter 50 value 561.033418
## iter 60 value 552.523654
## iter 70 value 549.052155
## iter 80 value 547.739633
## iter 90 value 547.233831
## iter 100 value 545.420098
## final value 545.420098
## stopped after 100 iterations
## # weights: 11
## initial value 5905.170888
## iter 10 value 3187.662770
## iter 20 value 1199.102254
## iter 30 value 841.893189
## iter 40 value 701.546332
## iter 50 value 678.195966
## iter 60 value 674.255705
## iter 70 value 672.336805
## iter 80 value 672.295783
## iter 90 value 672.168200
## final value 672.159111
## converged
## # weights: 31
## initial value 4977.594584
## iter 10 value 773.361297
## iter 20 value 660.446010
## iter 30 value 627.851986
## iter 40 value 618.380383
## iter 50 value 616.935455
## iter 60 value 614.655193
## iter 70 value 611.480600
## iter 80 value 610.308459
## iter 90 value 610.014292
## iter 100 value 609.800610
## final value 609.800610
## stopped after 100 iterations
## # weights: 51
## initial value 9039.841190
## iter 10 value 956.956985
## iter 20 value 669.468368
## iter 30 value 626.044387
## iter 40 value 612.456868
## iter 50 value 605.678528
## iter 60 value 600.435617
## iter 70 value 592.539326
## iter 80 value 586.454385
## iter 90 value 583.052898
## iter 100 value 582.235772
## final value 582.235772
## stopped after 100 iterations
## # weights: 11
## initial value 4625.872319
## iter 10 value 1150.156927
## iter 20 value 713.466807
## iter 30 value 703.341801
## iter 40 value 695.151359
## final value 695.123864
## converged
## # weights: 31
## initial value 5577.117366
## iter 10 value 847.734987
## iter 20 value 687.646900
## iter 30 value 655.253722
## iter 40 value 646.895100
## iter 50 value 642.047174
## iter 60 value 633.771989
## iter 70 value 630.883911
## iter 80 value 629.346224
## iter 90 value 625.593200
## iter 100 value 624.214842
## final value 624.214842
## stopped after 100 iterations
## # weights: 51
## initial value 5076.194896
## iter 10 value 717.101801
## iter 20 value 646.412256
## iter 30 value 633.258688
## iter 40 value 629.697694
## iter 50 value 623.443517
## iter 60 value 621.767877
## iter 70 value 620.208557
## iter 80 value 619.649614
## iter 90 value 618.651644
## iter 100 value 617.242462
## final value 617.242462
## stopped after 100 iterations
## # weights: 11
## initial value 3936.101780
## iter 10 value 1358.535887
## iter 20 value 827.425198
## iter 30 value 804.214734
## iter 40 value 707.394161
## iter 50 value 679.216948
## iter 60 value 674.291987
## iter 70 value 672.375306
## iter 80 value 672.358692
## iter 90 value 672.210586
## iter 100 value 672.204277
## final value 672.204277
## stopped after 100 iterations
## # weights: 31
## initial value 5522.672194
## iter 10 value 903.079649
## iter 20 value 704.989416
## iter 30 value 659.981533
## iter 40 value 650.190976
## iter 50 value 645.599249
## iter 60 value 644.290566
## iter 70 value 642.274724
## iter 80 value 641.207188
## iter 90 value 640.575577
## iter 100 value 639.218266
## final value 639.218266
## stopped after 100 iterations
## # weights: 51
## initial value 13288.728111
## iter 10 value 795.084849
## iter 20 value 656.551629
## iter 30 value 617.527905
## iter 40 value 607.228413
## iter 50 value 599.400294
## iter 60 value 593.708048
## iter 70 value 589.008004
## iter 80 value 584.634214
## iter 90 value 583.751787
## iter 100 value 582.725971
## final value 582.725971
## stopped after 100 iterations
## # weights: 11
## initial value 7635.422123
## iter 10 value 2694.046682
## iter 20 value 934.393810
## iter 30 value 909.624045
## iter 40 value 861.735324
## iter 50 value 848.679741
## iter 60 value 829.021820
## iter 70 value 826.463236
## iter 80 value 825.365915
## iter 90 value 824.812313
## iter 100 value 824.297298
## final value 824.297298
## stopped after 100 iterations
## # weights: 31
## initial value 9451.324568
## iter 10 value 717.145118
## iter 20 value 648.296103
## iter 30 value 603.125551
## iter 40 value 585.098871
## iter 50 value 580.134389
## iter 60 value 579.060929
## iter 70 value 578.441133
## iter 80 value 576.552377
## iter 90 value 574.017986
## iter 100 value 573.204193
## final value 573.204193
## stopped after 100 iterations
## # weights: 51
## initial value 4780.303779
## iter 10 value 676.021208
## iter 20 value 605.680541
## iter 30 value 580.623543
## iter 40 value 574.107507
## iter 50 value 563.588873
## iter 60 value 559.403357
## iter 70 value 555.457647
## iter 80 value 553.107130
## iter 90 value 550.555294
## iter 100 value 549.285614
## final value 549.285614
## stopped after 100 iterations
## # weights: 11
## initial value 4954.285928
## iter 10 value 1285.167814
## iter 20 value 801.416817
## iter 30 value 752.538812
## iter 40 value 673.518026
## iter 50 value 667.833248
## iter 60 value 667.426580
## final value 667.426329
## converged
## # weights: 31
## initial value 8509.528417
## iter 10 value 794.459533
## iter 20 value 690.540864
## iter 30 value 659.126465
## iter 40 value 635.484225
## iter 50 value 615.624413
## iter 60 value 613.334470
## iter 70 value 611.579778
## iter 80 value 608.782971
## iter 90 value 606.673171
## iter 100 value 606.543584
## final value 606.543584
## stopped after 100 iterations
## # weights: 51
## initial value 5179.198600
## iter 10 value 674.835958
## iter 20 value 612.648959
## iter 30 value 592.999501
## iter 40 value 587.551719
## iter 50 value 585.656890
## iter 60 value 584.858718
## iter 70 value 584.512698
## iter 80 value 584.472829
## iter 90 value 584.445231
## iter 100 value 584.434653
## final value 584.434653
## stopped after 100 iterations
## # weights: 11
## initial value 7180.780172
## iter 10 value 1263.301124
## iter 20 value 822.911580
## iter 30 value 670.227168
## iter 40 value 646.251715
## iter 50 value 646.185550
## iter 60 value 646.174296
## iter 70 value 646.144082
## final value 646.143922
## converged
## # weights: 31
## initial value 7999.670417
## iter 10 value 781.541320
## iter 20 value 655.634660
## iter 30 value 624.646468
## iter 40 value 615.338281
## iter 50 value 609.019319
## iter 60 value 604.652826
## iter 70 value 603.309983
## iter 80 value 602.092699
## iter 90 value 599.857177
## iter 100 value 595.359841
## final value 595.359841
## stopped after 100 iterations
## # weights: 51
## initial value 7120.713689
## iter 10 value 641.859521
## iter 20 value 594.780738
## iter 30 value 581.237050
## iter 40 value 576.188800
## iter 50 value 568.992790
## iter 60 value 567.459860
## iter 70 value 565.655482
## iter 80 value 563.904886
## iter 90 value 562.364833
## iter 100 value 561.726107
## final value 561.726107
## stopped after 100 iterations
## # weights: 11
## initial value 6807.712374
## iter 10 value 946.910040
## iter 20 value 759.105371
## iter 30 value 724.278038
## iter 40 value 669.823285
## iter 50 value 661.053709
## iter 60 value 658.832705
## iter 70 value 657.344605
## iter 80 value 657.306894
## iter 90 value 657.038667
## iter 100 value 657.017384
## final value 657.017384
## stopped after 100 iterations
## # weights: 31
## initial value 5168.095501
## iter 10 value 813.256362
## iter 20 value 652.854846
## iter 30 value 630.365623
## iter 40 value 610.241918
## iter 50 value 600.530244
## iter 60 value 596.238726
## iter 70 value 592.166489
## iter 80 value 590.367941
## iter 90 value 589.569891
## iter 100 value 589.329555
## final value 589.329555
## stopped after 100 iterations
## # weights: 51
## initial value 8206.092098
## iter 10 value 719.356227
## iter 20 value 622.161104
## iter 30 value 589.289002
## iter 40 value 584.268697
## iter 50 value 580.364287
## iter 60 value 574.299286
## iter 70 value 568.848539
## iter 80 value 566.989195
## iter 90 value 566.050131
## iter 100 value 564.296105
## final value 564.296105
## stopped after 100 iterations
## # weights: 11
## initial value 4068.064929
## iter 10 value 1596.611603
## iter 20 value 920.962212
## iter 30 value 814.812838
## iter 40 value 690.717264
## iter 50 value 678.989448
## iter 60 value 677.608177
## final value 677.600820
## converged
## # weights: 31
## initial value 5419.595588
## iter 10 value 787.501096
## iter 20 value 695.951188
## iter 30 value 666.137929
## iter 40 value 642.502472
## iter 50 value 625.686966
## iter 60 value 623.255576
## iter 70 value 619.481555
## iter 80 value 615.804389
## iter 90 value 613.755216
## iter 100 value 613.700000
## final value 613.700000
## stopped after 100 iterations
## # weights: 51
## initial value 7023.412724
## iter 10 value 715.906202
## iter 20 value 650.083821
## iter 30 value 631.500789
## iter 40 value 621.188239
## iter 50 value 615.443840
## iter 60 value 607.500484
## iter 70 value 603.289938
## iter 80 value 601.614755
## iter 90 value 600.210303
## iter 100 value 596.764482
## final value 596.764482
## stopped after 100 iterations
## # weights: 11
## initial value 7576.966211
## iter 10 value 3567.495654
## iter 20 value 1114.469421
## iter 30 value 871.712165
## iter 40 value 842.512156
## iter 50 value 824.431159
## iter 60 value 789.155009
## iter 70 value 703.688181
## iter 80 value 671.197836
## iter 90 value 660.250517
## iter 100 value 658.212099
## final value 658.212099
## stopped after 100 iterations
## # weights: 31
## initial value 4774.489936
## iter 10 value 787.468681
## iter 20 value 680.902837
## iter 30 value 644.069073
## iter 40 value 619.074307
## iter 50 value 610.785171
## iter 60 value 609.247589
## iter 70 value 607.085391
## iter 80 value 602.942508
## iter 90 value 600.408462
## iter 100 value 598.765421
## final value 598.765421
## stopped after 100 iterations
## # weights: 51
## initial value 5778.887569
## iter 10 value 1218.678828
## iter 20 value 764.308351
## iter 30 value 700.805814
## iter 40 value 670.019739
## iter 50 value 656.469915
## iter 60 value 643.130541
## iter 70 value 634.538639
## iter 80 value 613.051049
## iter 90 value 603.331068
## iter 100 value 601.525414
## final value 601.525414
## stopped after 100 iterations
## # weights: 11
## initial value 5456.730539
## iter 10 value 2279.142109
## iter 20 value 1357.419809
## iter 30 value 868.384479
## iter 40 value 798.742787
## iter 50 value 692.168373
## iter 60 value 663.161565
## iter 70 value 654.653541
## iter 80 value 654.109752
## iter 90 value 653.766024
## final value 653.735752
## converged
## # weights: 31
## initial value 5932.907552
## iter 10 value 695.033683
## iter 20 value 631.635507
## iter 30 value 612.568059
## iter 40 value 598.134876
## iter 50 value 593.120867
## iter 60 value 591.823090
## iter 70 value 591.646897
## iter 80 value 591.508744
## iter 90 value 591.467211
## iter 100 value 591.450479
## final value 591.450479
## stopped after 100 iterations
## # weights: 51
## initial value 6679.611746
## iter 10 value 735.241151
## iter 20 value 637.304011
## iter 30 value 590.501946
## iter 40 value 576.393548
## iter 50 value 567.774734
## iter 60 value 562.874621
## iter 70 value 558.914367
## iter 80 value 556.011048
## iter 90 value 554.050695
## iter 100 value 552.447979
## final value 552.447979
## stopped after 100 iterations
## # weights: 11
## initial value 5828.593212
## iter 10 value 1636.119986
## iter 20 value 851.303337
## iter 30 value 710.008726
## iter 40 value 677.983002
## iter 50 value 671.284508
## final value 671.270279
## converged
## # weights: 31
## initial value 5768.516195
## iter 10 value 696.136831
## iter 20 value 662.322956
## iter 30 value 633.303869
## iter 40 value 622.964996
## iter 50 value 619.170305
## iter 60 value 616.297723
## iter 70 value 615.719626
## iter 80 value 615.347216
## iter 90 value 615.324765
## final value 615.324543
## converged
## # weights: 51
## initial value 7598.417288
## iter 10 value 679.118989
## iter 20 value 626.443527
## iter 30 value 615.012291
## iter 40 value 607.271768
## iter 50 value 603.450333
## iter 60 value 601.483748
## iter 70 value 598.120955
## iter 80 value 594.448771
## iter 90 value 593.197212
## iter 100 value 592.850254
## final value 592.850254
## stopped after 100 iterations
## # weights: 11
## initial value 5055.150125
## iter 10 value 1782.024610
## iter 20 value 731.693354
## iter 30 value 683.814630
## iter 40 value 658.361278
## iter 50 value 654.615888
## iter 60 value 654.045784
## iter 70 value 653.783106
## final value 653.783063
## converged
## # weights: 31
## initial value 4089.297914
## iter 10 value 708.234121
## iter 20 value 633.253702
## iter 30 value 612.649719
## iter 40 value 605.817292
## iter 50 value 601.003874
## iter 60 value 597.908918
## iter 70 value 595.501119
## iter 80 value 593.708926
## iter 90 value 591.738607
## iter 100 value 588.619919
## final value 588.619919
## stopped after 100 iterations
## # weights: 51
## initial value 4707.450010
## iter 10 value 725.193462
## iter 20 value 623.526203
## iter 30 value 599.673296
## iter 40 value 581.788199
## iter 50 value 576.722361
## iter 60 value 569.836925
## iter 70 value 565.504729
## iter 80 value 562.855245
## iter 90 value 559.959214
## iter 100 value 556.641036
## final value 556.641036
## stopped after 100 iterations
## # weights: 11
## initial value 5247.392528
## iter 10 value 1384.332498
## iter 20 value 807.645635
## iter 30 value 721.918687
## iter 40 value 680.282236
## iter 50 value 667.141792
## iter 60 value 665.388618
## iter 70 value 664.658140
## iter 80 value 664.069916
## iter 90 value 664.064007
## iter 100 value 664.012381
## final value 664.012381
## stopped after 100 iterations
## # weights: 31
## initial value 11226.812352
## iter 10 value 851.084825
## iter 20 value 673.715919
## iter 30 value 634.628476
## iter 40 value 622.017335
## iter 50 value 618.761392
## iter 60 value 616.815482
## iter 70 value 616.474401
## iter 80 value 615.864581
## iter 90 value 614.802248
## iter 100 value 613.619087
## final value 613.619087
## stopped after 100 iterations
## # weights: 51
## initial value 6808.710850
## iter 10 value 763.275600
## iter 20 value 640.273933
## iter 30 value 612.232951
## iter 40 value 596.500066
## iter 50 value 588.262421
## iter 60 value 585.474981
## iter 70 value 582.597330
## iter 80 value 578.703887
## iter 90 value 577.542691
## iter 100 value 575.276144
## final value 575.276144
## stopped after 100 iterations
## # weights: 11
## initial value 5792.264820
## iter 10 value 1992.516059
## iter 20 value 1001.998397
## iter 30 value 713.258028
## iter 40 value 686.257674
## final value 684.173325
## converged
## # weights: 31
## initial value 6065.741322
## iter 10 value 1252.668430
## iter 20 value 898.126709
## iter 30 value 768.225342
## iter 40 value 672.334360
## iter 50 value 645.852456
## iter 60 value 636.771611
## iter 70 value 629.177360
## iter 80 value 626.142131
## iter 90 value 620.308145
## iter 100 value 619.673798
## final value 619.673798
## stopped after 100 iterations
## # weights: 51
## initial value 5763.975964
## iter 10 value 753.751966
## iter 20 value 660.823955
## iter 30 value 640.154677
## iter 40 value 632.526514
## iter 50 value 628.462376
## iter 60 value 622.046325
## iter 70 value 616.470297
## iter 80 value 607.901865
## iter 90 value 600.690167
## iter 100 value 599.175964
## final value 599.175964
## stopped after 100 iterations
## # weights: 11
## initial value 6226.021397
## iter 10 value 1726.099569
## iter 20 value 1249.484360
## iter 30 value 849.716648
## iter 40 value 793.274064
## iter 50 value 735.480783
## iter 60 value 675.628250
## iter 70 value 667.670729
## iter 80 value 665.573750
## iter 90 value 664.235496
## iter 100 value 664.222881
## final value 664.222881
## stopped after 100 iterations
## # weights: 31
## initial value 3887.481953
## iter 10 value 834.601418
## iter 20 value 675.566716
## iter 30 value 647.882493
## iter 40 value 639.064248
## iter 50 value 632.389123
## iter 60 value 622.008813
## iter 70 value 617.094144
## iter 80 value 613.713588
## iter 90 value 611.905341
## iter 100 value 606.640219
## final value 606.640219
## stopped after 100 iterations
## # weights: 51
## initial value 7560.810413
## iter 10 value 993.526192
## iter 20 value 680.036281
## iter 30 value 616.359947
## iter 40 value 584.085140
## iter 50 value 571.469888
## iter 60 value 569.421059
## iter 70 value 567.440115
## iter 80 value 565.703151
## iter 90 value 565.500423
## iter 100 value 565.334324
## final value 565.334324
## stopped after 100 iterations
## # weights: 11
## initial value 8058.391887
## iter 10 value 3713.113763
## iter 20 value 2737.156353
## iter 30 value 1691.305730
## iter 40 value 863.934528
## iter 50 value 858.287402
## iter 60 value 845.885200
## iter 70 value 833.580028
## iter 80 value 832.970331
## iter 90 value 830.312635
## iter 100 value 829.576342
## final value 829.576342
## stopped after 100 iterations
## # weights: 31
## initial value 4366.466135
## iter 10 value 714.761741
## iter 20 value 635.270166
## iter 30 value 617.018811
## iter 40 value 611.968251
## iter 50 value 609.083910
## iter 60 value 603.873763
## iter 70 value 602.181467
## iter 80 value 596.954090
## iter 90 value 587.155643
## iter 100 value 585.495874
## final value 585.495874
## stopped after 100 iterations
## # weights: 51
## initial value 5863.680647
## iter 10 value 742.749981
## iter 20 value 606.944030
## iter 30 value 584.817348
## iter 40 value 569.867375
## iter 50 value 560.833905
## iter 60 value 557.111766
## iter 70 value 550.721528
## iter 80 value 546.788259
## iter 90 value 544.162732
## iter 100 value 542.610535
## final value 542.610535
## stopped after 100 iterations
## # weights: 11
## initial value 5399.801661
## iter 10 value 875.446103
## iter 20 value 723.076458
## iter 30 value 678.920173
## iter 40 value 674.402655
## final value 674.082709
## converged
## # weights: 31
## initial value 5007.460881
## iter 10 value 741.160789
## iter 20 value 651.078124
## iter 30 value 624.063301
## iter 40 value 620.581732
## iter 50 value 615.403700
## iter 60 value 613.804946
## iter 70 value 612.995742
## iter 80 value 612.102142
## iter 90 value 610.202069
## iter 100 value 608.880710
## final value 608.880710
## stopped after 100 iterations
## # weights: 51
## initial value 4666.331590
## iter 10 value 690.817409
## iter 20 value 636.367663
## iter 30 value 622.622532
## iter 40 value 610.012525
## iter 50 value 604.803223
## iter 60 value 597.690909
## iter 70 value 592.946227
## iter 80 value 591.738058
## iter 90 value 591.222557
## iter 100 value 590.713983
## final value 590.713983
## stopped after 100 iterations
## # weights: 11
## initial value 6894.698966
## iter 10 value 992.972783
## iter 20 value 758.784412
## iter 30 value 709.106142
## iter 40 value 660.040331
## iter 50 value 652.122394
## iter 60 value 650.830911
## iter 70 value 648.848726
## iter 80 value 648.594873
## iter 90 value 648.364787
## iter 100 value 648.231917
## final value 648.231917
## stopped after 100 iterations
## # weights: 31
## initial value 7461.318880
## iter 10 value 916.812327
## iter 20 value 724.333070
## iter 30 value 656.916953
## iter 40 value 635.271605
## iter 50 value 621.815000
## iter 60 value 618.105472
## iter 70 value 612.145789
## iter 80 value 606.391184
## iter 90 value 605.010945
## iter 100 value 604.140135
## final value 604.140135
## stopped after 100 iterations
## # weights: 51
## initial value 6976.311308
## iter 10 value 711.054855
## iter 20 value 612.427701
## iter 30 value 578.818987
## iter 40 value 560.628521
## iter 50 value 550.776866
## iter 60 value 547.430771
## iter 70 value 545.826052
## iter 80 value 544.134733
## iter 90 value 541.272052
## iter 100 value 537.213649
## final value 537.213649
## stopped after 100 iterations
## # weights: 11
## initial value 4141.445159
## iter 10 value 2087.761113
## iter 20 value 991.823804
## iter 30 value 925.575087
## iter 40 value 885.771220
## iter 50 value 862.086193
## iter 60 value 845.711330
## iter 70 value 840.381496
## iter 80 value 836.603819
## iter 90 value 832.640382
## iter 100 value 829.196808
## final value 829.196808
## stopped after 100 iterations
## # weights: 31
## initial value 8625.020494
## iter 10 value 683.417099
## iter 20 value 610.586375
## iter 30 value 586.352308
## iter 40 value 582.365618
## iter 50 value 580.822323
## iter 60 value 580.485239
## iter 70 value 580.023295
## iter 80 value 579.193417
## iter 90 value 577.858303
## iter 100 value 576.780084
## final value 576.780084
## stopped after 100 iterations
## # weights: 51
## initial value 12310.441396
## iter 10 value 699.476656
## iter 20 value 602.877418
## iter 30 value 579.166563
## iter 40 value 570.973855
## iter 50 value 565.270697
## iter 60 value 551.302194
## iter 70 value 546.059335
## iter 80 value 540.675313
## iter 90 value 537.549064
## iter 100 value 535.856847
## final value 535.856847
## stopped after 100 iterations
## # weights: 11
## initial value 7225.436419
## iter 10 value 1238.503238
## iter 20 value 825.678758
## iter 30 value 768.576670
## iter 40 value 688.489003
## iter 50 value 654.796214
## iter 60 value 653.355040
## final value 653.300244
## converged
## # weights: 31
## initial value 7077.289882
## iter 10 value 820.946265
## iter 20 value 704.764749
## iter 30 value 663.794070
## iter 40 value 629.470949
## iter 50 value 619.380603
## iter 60 value 611.572749
## iter 70 value 608.759295
## iter 80 value 605.885361
## iter 90 value 603.777785
## iter 100 value 602.328790
## final value 602.328790
## stopped after 100 iterations
## # weights: 51
## initial value 6264.992573
## iter 10 value 1064.367595
## iter 20 value 784.061563
## iter 30 value 655.428478
## iter 40 value 608.477039
## iter 50 value 590.739125
## iter 60 value 582.290053
## iter 70 value 575.352275
## iter 80 value 570.790434
## iter 90 value 568.038272
## iter 100 value 564.286440
## final value 564.286440
## stopped after 100 iterations
## # weights: 11
## initial value 7647.558355
## iter 10 value 4600.824314
## iter 20 value 1135.738848
## iter 30 value 708.515755
## iter 40 value 657.277752
## iter 50 value 638.421887
## iter 60 value 634.993262
## iter 70 value 634.194980
## iter 80 value 633.609280
## iter 90 value 633.605938
## iter 100 value 633.556947
## final value 633.556947
## stopped after 100 iterations
## # weights: 31
## initial value 6534.835738
## iter 10 value 768.405427
## iter 20 value 654.370403
## iter 30 value 603.680480
## iter 40 value 581.547203
## iter 50 value 575.681688
## iter 60 value 575.289028
## iter 70 value 575.002361
## iter 80 value 574.818466
## iter 90 value 573.462546
## iter 100 value 572.862702
## final value 572.862702
## stopped after 100 iterations
## # weights: 51
## initial value 8107.770895
## iter 10 value 646.377980
## iter 20 value 589.580873
## iter 30 value 562.581768
## iter 40 value 550.044803
## iter 50 value 541.071572
## iter 60 value 539.222926
## iter 70 value 536.505165
## iter 80 value 527.540659
## iter 90 value 522.995964
## iter 100 value 521.333869
## final value 521.333869
## stopped after 100 iterations
## # weights: 11
## initial value 8396.219814
## iter 10 value 3062.648088
## iter 20 value 1199.345440
## iter 30 value 956.863196
## iter 40 value 935.372281
## iter 50 value 923.562838
## iter 60 value 897.624951
## iter 70 value 862.743541
## iter 80 value 843.885378
## iter 90 value 835.715208
## iter 100 value 830.965726
## final value 830.965726
## stopped after 100 iterations
## # weights: 31
## initial value 4253.152296
## iter 10 value 750.403197
## iter 20 value 658.984206
## iter 30 value 636.164164
## iter 40 value 622.905478
## iter 50 value 621.851069
## iter 60 value 620.552134
## iter 70 value 620.294374
## iter 80 value 619.643979
## iter 90 value 612.416513
## iter 100 value 608.480157
## final value 608.480157
## stopped after 100 iterations
## # weights: 51
## initial value 3676.374832
## iter 10 value 705.740956
## iter 20 value 602.071965
## iter 30 value 583.640937
## iter 40 value 575.411865
## iter 50 value 570.243901
## iter 60 value 564.337594
## iter 70 value 560.564203
## iter 80 value 557.065253
## iter 90 value 552.424798
## iter 100 value 550.701840
## final value 550.701840
## stopped after 100 iterations
## # weights: 11
## initial value 9214.259166
## iter 10 value 1772.603331
## iter 20 value 817.459266
## iter 30 value 710.352547
## iter 40 value 682.027333
## iter 50 value 676.126784
## iter 60 value 676.123103
## final value 676.122960
## converged
## # weights: 31
## initial value 8450.877400
## iter 10 value 847.873458
## iter 20 value 723.808154
## iter 30 value 685.442810
## iter 40 value 669.526376
## iter 50 value 659.116860
## iter 60 value 642.708400
## iter 70 value 633.537737
## iter 80 value 625.485233
## iter 90 value 619.411390
## iter 100 value 610.074559
## final value 610.074559
## stopped after 100 iterations
## # weights: 51
## initial value 5467.436044
## iter 10 value 1344.576777
## iter 20 value 913.279127
## iter 30 value 812.999385
## iter 40 value 688.293820
## iter 50 value 648.468054
## iter 60 value 628.598673
## iter 70 value 612.300143
## iter 80 value 602.457741
## iter 90 value 595.248227
## iter 100 value 590.970503
## final value 590.970503
## stopped after 100 iterations
## # weights: 11
## initial value 4700.960746
## iter 10 value 944.164478
## iter 20 value 840.769346
## iter 30 value 754.895667
## iter 40 value 681.274933
## iter 50 value 663.808041
## iter 60 value 660.668764
## iter 70 value 657.841042
## iter 80 value 657.616398
## iter 90 value 657.578553
## final value 657.576001
## converged
## # weights: 31
## initial value 8835.129149
## iter 10 value 697.450705
## iter 20 value 632.827985
## iter 30 value 611.860839
## iter 40 value 602.220740
## iter 50 value 598.581489
## iter 60 value 594.929097
## iter 70 value 594.160982
## iter 80 value 592.418625
## iter 90 value 590.967450
## iter 100 value 590.713576
## final value 590.713576
## stopped after 100 iterations
## # weights: 51
## initial value 9724.699865
## iter 10 value 794.082786
## iter 20 value 641.470162
## iter 30 value 602.813962
## iter 40 value 586.644477
## iter 50 value 576.580239
## iter 60 value 572.637118
## iter 70 value 568.825580
## iter 80 value 566.984774
## iter 90 value 565.631788
## iter 100 value 564.903619
## final value 564.903619
## stopped after 100 iterations
## # weights: 11
## initial value 6026.887371
## iter 10 value 1597.985289
## iter 20 value 821.268075
## iter 30 value 698.585312
## iter 40 value 680.648825
## iter 50 value 673.592686
## iter 60 value 673.152869
## iter 70 value 672.733846
## final value 672.717726
## converged
## # weights: 31
## initial value 5769.150330
## iter 10 value 794.676079
## iter 20 value 723.826529
## iter 30 value 689.651661
## iter 40 value 644.425468
## iter 50 value 637.870181
## iter 60 value 633.805424
## iter 70 value 630.377014
## iter 80 value 615.959198
## iter 90 value 606.429654
## iter 100 value 605.375568
## final value 605.375568
## stopped after 100 iterations
## # weights: 51
## initial value 3596.061409
## iter 10 value 869.209204
## iter 20 value 669.648390
## iter 30 value 634.869386
## iter 40 value 625.297759
## iter 50 value 621.234400
## iter 60 value 610.544442
## iter 70 value 603.022432
## iter 80 value 601.157953
## iter 90 value 599.757740
## iter 100 value 591.504283
## final value 591.504283
## stopped after 100 iterations
## # weights: 11
## initial value 4346.045266
## iter 10 value 1764.758539
## iter 20 value 868.201790
## iter 30 value 700.162505
## iter 40 value 691.836805
## iter 50 value 690.481889
## final value 690.481879
## converged
## # weights: 31
## initial value 8134.619849
## iter 10 value 970.140861
## iter 20 value 799.944647
## iter 30 value 672.697056
## iter 40 value 646.011824
## iter 50 value 630.883873
## iter 60 value 628.293278
## iter 70 value 627.141019
## iter 80 value 626.967560
## iter 90 value 626.876309
## iter 100 value 626.833136
## final value 626.833136
## stopped after 100 iterations
## # weights: 51
## initial value 9626.746277
## iter 10 value 810.208333
## iter 20 value 676.766177
## iter 30 value 660.572226
## iter 40 value 643.742526
## iter 50 value 635.462082
## iter 60 value 630.052860
## iter 70 value 627.469307
## iter 80 value 623.021971
## iter 90 value 618.179100
## iter 100 value 616.006820
## final value 616.006820
## stopped after 100 iterations
## # weights: 11
## initial value 5665.690169
## iter 10 value 1402.580340
## iter 20 value 936.432822
## iter 30 value 679.397470
## iter 40 value 676.139391
## iter 50 value 673.244622
## iter 60 value 672.786596
## iter 70 value 672.754444
## final value 672.749623
## converged
## # weights: 31
## initial value 3864.203775
## iter 10 value 895.184622
## iter 20 value 731.645634
## iter 30 value 678.022404
## iter 40 value 653.266824
## iter 50 value 648.594907
## iter 60 value 645.903586
## iter 70 value 644.607780
## iter 80 value 644.284239
## iter 90 value 643.300702
## iter 100 value 642.481790
## final value 642.481790
## stopped after 100 iterations
## # weights: 51
## initial value 6571.051268
## iter 10 value 776.174422
## iter 20 value 658.669025
## iter 30 value 609.868380
## iter 40 value 594.783418
## iter 50 value 590.297582
## iter 60 value 578.664441
## iter 70 value 572.603246
## iter 80 value 569.073044
## iter 90 value 566.517747
## iter 100 value 563.218690
## final value 563.218690
## stopped after 100 iterations
## # weights: 11
## initial value 5386.380969
## iter 10 value 1056.574968
## iter 20 value 865.591802
## iter 30 value 836.353315
## iter 40 value 794.180783
## iter 50 value 688.773770
## iter 60 value 667.179441
## iter 70 value 661.959768
## iter 80 value 659.624531
## iter 90 value 659.570385
## iter 100 value 659.219128
## final value 659.219128
## stopped after 100 iterations
## # weights: 31
## initial value 3472.543484
## iter 10 value 726.185331
## iter 20 value 642.269569
## iter 30 value 624.785045
## iter 40 value 613.183697
## iter 50 value 602.911170
## iter 60 value 597.918862
## iter 70 value 595.688668
## iter 80 value 593.985966
## iter 90 value 592.500971
## iter 100 value 591.908376
## final value 591.908376
## stopped after 100 iterations
## # weights: 51
## initial value 6427.531606
## iter 10 value 704.389430
## iter 20 value 615.915426
## iter 30 value 596.763810
## iter 40 value 588.243734
## iter 50 value 583.601040
## iter 60 value 581.440703
## iter 70 value 579.061288
## iter 80 value 577.471906
## iter 90 value 575.309641
## iter 100 value 573.302504
## final value 573.302504
## stopped after 100 iterations
## # weights: 11
## initial value 3824.290300
## iter 10 value 1019.692941
## iter 20 value 715.371286
## iter 30 value 683.347322
## iter 40 value 679.206199
## final value 679.202486
## converged
## # weights: 31
## initial value 7272.649229
## iter 10 value 1418.191731
## iter 20 value 1193.329007
## iter 30 value 962.346340
## iter 40 value 732.281048
## iter 50 value 682.377179
## iter 60 value 658.094525
## iter 70 value 645.389435
## iter 80 value 634.702077
## iter 90 value 624.037258
## iter 100 value 621.259394
## final value 621.259394
## stopped after 100 iterations
## # weights: 51
## initial value 7972.885546
## iter 10 value 788.134044
## iter 20 value 666.101019
## iter 30 value 620.793420
## iter 40 value 604.762459
## iter 50 value 601.769335
## iter 60 value 598.276869
## iter 70 value 595.428835
## iter 80 value 590.261997
## iter 90 value 586.906804
## iter 100 value 586.186825
## final value 586.186825
## stopped after 100 iterations
## # weights: 11
## initial value 9160.607706
## iter 10 value 2284.342572
## iter 20 value 1964.922681
## iter 30 value 1508.142617
## iter 40 value 842.776418
## iter 50 value 666.902618
## iter 60 value 660.240112
## iter 70 value 659.893004
## iter 80 value 659.298076
## iter 90 value 659.266075
## iter 100 value 659.243847
## final value 659.243847
## stopped after 100 iterations
## # weights: 31
## initial value 4773.358379
## iter 10 value 692.542508
## iter 20 value 644.237924
## iter 30 value 611.237347
## iter 40 value 600.023317
## iter 50 value 594.120169
## iter 60 value 592.182438
## iter 70 value 591.730109
## iter 80 value 591.137418
## iter 90 value 590.835924
## iter 100 value 590.117832
## final value 590.117832
## stopped after 100 iterations
## # weights: 51
## initial value 5500.916626
## iter 10 value 895.399922
## iter 20 value 699.776997
## iter 30 value 622.793253
## iter 40 value 593.752374
## iter 50 value 585.004828
## iter 60 value 575.812572
## iter 70 value 571.690240
## iter 80 value 570.426521
## iter 90 value 568.592568
## iter 100 value 567.116870
## final value 567.116870
## stopped after 100 iterations
## # weights: 11
## initial value 10556.730906
## iter 10 value 2373.695783
## iter 20 value 2353.631517
## iter 20 value 2353.631509
## iter 20 value 2353.631504
## final value 2353.631504
## converged
## # weights: 31
## initial value 4679.857616
## iter 10 value 899.337959
## iter 20 value 741.758741
## iter 30 value 696.357267
## iter 40 value 659.894583
## iter 50 value 649.955032
## iter 60 value 638.918575
## iter 70 value 633.792745
## iter 80 value 630.517832
## iter 90 value 625.764714
## iter 100 value 621.489045
## final value 621.489045
## stopped after 100 iterations
## # weights: 51
## initial value 4629.404283
## iter 10 value 769.003654
## iter 20 value 662.842099
## iter 30 value 635.998071
## iter 40 value 618.302643
## iter 50 value 611.994903
## iter 60 value 609.564544
## iter 70 value 608.933118
## iter 80 value 606.367414
## iter 90 value 604.789817
## iter 100 value 602.049999
## final value 602.049999
## stopped after 100 iterations
## # weights: 11
## initial value 4813.169441
## iter 10 value 1780.958780
## iter 20 value 789.299328
## iter 30 value 718.029150
## iter 40 value 695.334608
## iter 50 value 694.158734
## final value 694.155954
## converged
## # weights: 31
## initial value 4854.004345
## iter 10 value 768.852988
## iter 20 value 706.150747
## iter 30 value 669.230669
## iter 40 value 642.988465
## iter 50 value 636.534779
## iter 60 value 635.192807
## iter 70 value 632.646071
## iter 80 value 630.259418
## iter 90 value 630.028640
## final value 630.027770
## converged
## # weights: 51
## initial value 12719.270621
## iter 10 value 755.382741
## iter 20 value 668.394528
## iter 30 value 633.030718
## iter 40 value 629.331670
## iter 50 value 625.890962
## iter 60 value 622.518252
## iter 70 value 620.339306
## iter 80 value 616.409376
## iter 90 value 613.883676
## iter 100 value 613.096646
## final value 613.096646
## stopped after 100 iterations
## # weights: 11
## initial value 7839.508846
## iter 10 value 1479.461732
## iter 20 value 807.272752
## iter 30 value 777.322514
## iter 40 value 731.647587
## iter 50 value 683.792180
## iter 60 value 680.221478
## iter 70 value 676.243893
## iter 80 value 675.887130
## iter 90 value 675.667650
## iter 100 value 675.644844
## final value 675.644844
## stopped after 100 iterations
## # weights: 31
## initial value 5597.370895
## iter 10 value 717.118390
## iter 20 value 648.917452
## iter 30 value 630.367121
## iter 40 value 622.699500
## iter 50 value 615.775533
## iter 60 value 608.946275
## iter 70 value 605.309093
## iter 80 value 604.405546
## iter 90 value 603.795481
## iter 100 value 603.623427
## final value 603.623427
## stopped after 100 iterations
## # weights: 51
## initial value 11915.677097
## iter 10 value 755.401790
## iter 20 value 658.025324
## iter 30 value 605.808044
## iter 40 value 596.928152
## iter 50 value 592.401592
## iter 60 value 589.423675
## iter 70 value 583.974357
## iter 80 value 581.451502
## iter 90 value 580.207894
## iter 100 value 579.107071
## final value 579.107071
## stopped after 100 iterations
## # weights: 51
## initial value 5755.943772
## iter 10 value 790.834571
## iter 20 value 720.072811
## iter 30 value 696.092171
## iter 40 value 686.576388
## iter 50 value 680.072560
## iter 60 value 676.477814
## iter 70 value 668.223681
## iter 80 value 663.902133
## iter 90 value 661.848357
## iter 100 value 660.724666
## final value 660.724666
## stopped after 100 iterations #Predictions on the test set
nnet_predictions_test <-predict(nnet_model, test)
# Confusion matrix on test set
table(test$target_class, nnet_predictions_test)## nnet_predictions_test
## 0 1
## 0 2261 14
## 1 36 194 # Accuracy of testing data:
(2261+194)/nrow(test) #accuracy = (TP+TN)/(TP+TN+FP+FN)## [1] 0.9800399 plot(nnet_predictions_test, main="Model Accuracies with Neural Networks")
varimp_NN <- varImp(nnet_model)
plot(varimp_NN, main="Variable Importance with Neural Networks")
# Summarize results
confusionMatrix(nnet_predictions_test, as.factor(test[,9]))## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 2261 36
## 1 14 194
##
## Accuracy : 0.98
## 95% CI : (0.9738, 0.9851)
## No Information Rate : 0.9082
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8749
##
## Mcnemar's Test P-Value : 0.002979
##
## Sensitivity : 0.9938
## Specificity : 0.8435
## Pos Pred Value : 0.9843
## Neg Pred Value : 0.9327
## Prevalence : 0.9082
## Detection Rate : 0.9026
## Detection Prevalence : 0.9170
## Balanced Accuracy : 0.9187
##
## 'Positive' Class : 0
##